Clinical Decision Support

Octagos completes the cardiac implantable device monitoring set alongside Implicity and PaceMate, and it takes the third distinct position. Implicity clears algorithms and publishes their sensitivity. PaceMate sells operations, billing and a security certification. Octagos sells the model and the humans reviewing it as a single indivisible product, which it brands the Two Brain Approach. Atlas AI is the engine. It reads incoming device transmissions, filters non actionable alerts, prioritises what needs clinical attention and surfaces findings across a clinic's population, with the company reporting clinic noise reduced by more than 25 percent and reports delivered at 98 percent accuracy. The company is explicit that automation alone is not enough, and pairs the model with United States based specialists certified by the International Board of Heart Rhythm Examiners who review alongside it. Its framing of the boundary is unusually direct: clinical teams review the model's output rather than handing judgment over to it. The headline performance claim is the highest in this segment and is attributed to a study the company describes as published in the Journal of the American College of Cardiology, reporting more than 99 percent accuracy, sensitivity and specificity for the combined system. Those figures describe the model and its human reviewers together rather than the algorithm alone, which the company does not obscure but which a reader comparing against Implicity's algorithm only sensitivity of 98.3 percent should hold in mind. Scope extends past implanted devices to ambulatory electrocardiographic monitoring, addressing a real fragmentation problem since those two workflows usually sit in separate systems. Around the platform sit a clinic mobile application, bidirectional integrations with major record systems including a named ambulatory system, automated billing and reporting, and a patient connectivity team whose job is keeping patients transmitting at all. Ask Atlas is the most distinctive recent capability. It lets a device clinic query its own monitoring data in plain language across patient care, operations, research and revenue, with the company's own examples running from which patients are overdue for a remote check to showing every actionable red alert this month. That is a natural language interface over a clinic's own population, and no other record in this segment offers it. Based in Houston, Texas, with a cardiac electrophysiologist serving as chief medical and compliance officer. The platform became available through the Microsoft commercial marketplace in February 2026. One thing a reader should weigh. Much of the comparative material about competitors in this segment is published by Octagos itself, which is useful and interested at the same time.

Implicity solves a problem the two neighbouring cardiac records do not have. iRhythm and AliveCor collect their own data from their own hardware. Implicity collects nothing: it takes the data cardiac implantable electronic devices already transmit, from every manufacturer, and applies models to decide which of it a clinician should actually look at. The underlying problem is alert burden. Pacemakers, defibrillators, resynchronisation devices and insertable cardiac monitors transmit continuously, each manufacturer uses its own portal and format, and a clinic following several hundred patients receives a volume of notifications in which the clinically important events are buried. Published work cited by the company puts atrial arrhythmia episodes at up to 51 percent of the events clinicians are expected to review. The platform aggregates, normalises and standardises data from Abbott, Biotronik, Boston Scientific, Medtronic and Microport into one interface, working from raw discrete manufacturer data rather than the summary documents the portals produce. Three cleared algorithms sit on top. The ILR ECG Analyzer received FDA 510(k) clearance in 2021 for assessing arrhythmias in insertable cardiac monitor data, initially Medtronic devices, and the company reports it reduces false positives by 79 percent while maintaining 99 percent sensitivity. An atrial fibrillation alert management algorithm filters device notifications and classifies episodes against European Society of Cardiology recommendations, with a retrospective study of more than 4,000 patients across four manufacturers published in the Cardiovascular Digital Health Journal, and a reported 85 percent reduction in unnecessary alerts. SignalHF analyses physiological trends including thoracic impedance, nighttime heart rate and activity to identify early signs of cardiac decompensation, and the company describes it as the first algorithm of its kind cleared and compatible across manufacturers. Evidence presented at EHRA 2026 is unusually candid. Across implantable cardiac monitor generations the algorithm held sensitivity at 98.3 percent in devices with their own onboard artificial intelligence and 94.3 percent in those without, with specificity of 61.6 and 75.6 percent respectively and positive predictive value around 74 percent in both. Publishing specificity in the sixties alongside high sensitivity is a vendor showing the actual shape of the tradeoff. Co founded by cardiac electrophysiologist Arnaud Rosier, with headquarters in Paris and Cambridge, Massachusetts. The company reports more than 110,000 patients monitored across over 250 medical facilities in the United States and Europe, and states access to the French government's Health Data Hub, described as one of the world's largest heart disease patient databases, for model development.

ClosedLoop builds predictive models for healthcare organisations and explains them. The platform supplies off the shelf models for common use cases, automates the data preparation that consumes most of a healthcare data scientist's time, and produces individual level risk predictions rather than population scores. Named applications include predicting admissions and readmissions, total utilisation and total risk, emergency department over utilisation, out of network utilisation, appointment non attendance, and the onset or progression of chronic disease. The distinguishing credential is competitive and adjudicated rather than self reported. In April 2021 the company won the grand prize in the Centers for Medicare and Medicaid Services Artificial Intelligence Health Outcomes Challenge, a multi stage competition begun in 2019 and run by the agency's innovation centre with the American Academy of Family Physicians and Arnold Ventures. It beat more than 300 entrants including IBM, Mayo Clinic, Merck, Accenture and Deloitte, with Geisinger the runner up. What the challenge asked for matters as much as who won it: the brief was explicitly for explainable artificial intelligence that front line clinicians could understand and trust, applied to predicting unplanned admissions and adverse events among Medicare beneficiaries. An independent federal body ran a two year contest on explainability and this company came first. That design shows in the product. Each Patient Health Forecast presents a prediction alongside the specific variables driving it and links to interventions a clinical team can act on, so a clinician receives a reason and a next step rather than a number. Independent recognition continued afterwards, with the top rating in a major analyst firm's healthcare artificial intelligence data science category in 2022, 2023 and 2024. Published work includes an open sourced vulnerability index released during the pandemic, documented in a medical artificial intelligence journal and stated to have reached more than 10 million lives, and a collaboration with a value based primary care provider published in a clinical management journal. Named customers span a large nonprofit health plan, the largest Medicaid accountable care organisation, a long term care accountable care organisation, physician groups and a federal contractor. Based in Austin, Texas, with roughly 48 million dollars raised including a 34 million dollar Series B in August 2021. Two cautions. One named customer also participated as an investor in that round, so any reference from it carries a commercial interest. And no company announcement, funding event or product release was located after February 2024, which is a gap a buyer should resolve directly before relying on current capability. No pricing, security attestation or data handling statement was found.

DermaSensor is a handheld instrument that a family doctor points at a suspicious mole. It uses elastic scattering spectroscopy to record how light scatters through tissue beneath the lesion, and a proprietary algorithm converts that optical signal into a risk result displayed on the device screen within seconds. The indication is narrow and deliberate: use by non dermatologist physicians, on lesions raising concern for melanoma, basal cell carcinoma or squamous cell carcinoma, in patients aged 40 and above. The regulatory milestone is the strongest in this index. On 17 January 2024 the device received De Novo authorisation, having previously held Breakthrough Device designation, making it the first artificial intelligence enabled device authorised for skin cancer detection in the primary care setting. De Novo is the harder route because no predicate device exists, so the pathway creates a new classification rather than borrowing one. It is also CE marked. The evidence behind it is a genuine clinical trial programme. Ten studies enrolled more than 1,300 subjects across more than 30 sites with over 2,000 pathology verified lesions. The pivotal DERM-SUCCESS study was international, multicentre, prospective and blinded, running at 22 sites with 30 primary care physicians across 1,005 patients and 1,579 lesions. Three separate multi reader multi case studies involving more than 280 primary care physicians measured what actually matters, which is whether the device changes physician behaviour: aided physicians correctly referred 91.4 percent of cancers against 82.0 percent unaided. The operating characteristics are published, and a buyer needs both halves of them. Sensitivity was 96.3 percent across common skin cancers, standalone melanoma sensitivity 90.2 percent, negative predictive value 98.1 percent for melanoma and 96.6 percent overall. Specificity was 20.7 percent. That last figure is the operational reality of the device and it appears far less prominently in company material than the sensitivity does: roughly four in five benign lesions will return a positive result. The device is built to avoid missing cancers, and it pays for that with referrals. One disclosure deserves specific credit. The device is authorised across all Fitzpatrick skin types, clinical studies included patients across the full range, and performance is stated as consistent regardless of skin pigmentation. Skin tone is the single most documented failure mode in dermatology artificial intelligence, and few products address it with regulatory backing. The authorised algorithm is also locked and cannot be changed without new studies and new authorisation. Labelling is explicit about what this is not: not a screening tool, not a sole diagnostic criterion, and not intended to confirm a diagnosis. Based in Miami. Commercial launch came in mid 2024, with more than 20,000 lesions scanned and hundreds of physicians using the device by late 2025. A 16 million dollar Series B closed in October 2025 taking total investment to 43 million. Commercially it is sold as a usage based subscription with a one time device setup and activation fee.

contextflow reads chest computed tomography and shows the radiologist comparable cases rather than only a verdict. ADVANCE Chest CT provides computer aided detection support across suspected lung cancer, interstitial lung disease and chronic obstructive pulmonary disease, combining nodule detection and quantification, nodule tracking across time, quantitative lung tissue analysis, and qualitative assessment of 19 named image patterns. Alongside those findings it surfaces reference cases and differential diagnosis information drawn from its founding technology, a three dimensional image based search engine that retrieves visually similar cases from a reference database. That retrieval design is the company's distinguishing position and it is deliberate. Public material and customer commentary both frame the product against black box artificial intelligence, and showing a radiologist several comparable prior cases is a materially different kind of support from issuing a probability. The findings arrive inside the radiologist's own native viewer rather than in a separate application. The company is a spinout of the Medical University of Vienna and the European KHRESMOI research project, supported by the Technical University of Vienna, founded in July 2016 by Markus Holzer, Georg Langs, René Donner and Allan Hanbury. Roughly 14 million dollars was raised across two rounds including a 6.7 million euro Series A in 2021 and a 1.2 million euro European Commission grant in 2020. An advisory board draws on leadership from the European and International radiology societies. Regulatory standing is European and singular. ADVANCE Chest CT is CE certified under the current medical device regulation, a pathway the company has publicly described as requiring substantial investment in quality management, and which a number of legacy products did not survive. No United States clearance was located and no United States market presence was found. Ownership changed in June 2026. 4DMedical Limited, an Australian listed respiratory imaging company, signed a binding agreement to acquire contextflow for more than 11.4 million euros, gaining a European commercial and technical team, established clinical relationships and a CE marked portfolio already in routine use. Two things follow that a reader should hold together. The acquirer holds United States clearances of its own, so the combination could open a market contextflow could not reach alone. But the same acquirer bought Imbio in 2023 and now describes that company's capabilities as part of its own portfolio rather than as a continuing brand, which is the pattern that ends a separate record. This vendor is indexed on its own because the acquisition is recent and the product remains named and marketed, and brand persistence beyond the transition has not been verified. Disclosure outside the clinical and regulatory story is thin. A dedicated pass located no pricing, no security attestation, no data handling statement, no deployment or hosting description, no customer count and no named deployment scale figure.

icometrix measures the brain. Its icobrain software automatically detects, segments and quantifies brain structures and lesions from magnetic resonance and computed tomography images, then reports those measurements against an age and gender matched reference population so a clinician can see whether a given brain differs from expectation and whether it is changing over time. Longitudinal comparison across scans acquired at different times is patented and is the capability the company was built around. Coverage is unusually wide for imaging artificial intelligence. Modules address multiple sclerosis, dementia and Alzheimer's disease, amyloid related imaging abnormality monitoring, traumatic brain injury, stroke, epilepsy, brain tumours and Parkinson's disease, with a portfolio described as eight regulatory approved solutions. Alongside icobrain sit icobridge, which connects magnetic resonance scanners, picture archiving systems and record systems and handles transfer, report generation and dashboards, and icompanion, a patient facing application for people with multiple sclerosis. The regulatory record runs a decade. First United States clearance came in September 2016, European marking followed under the newer medical device regulation pathway, and in June 2026 icobrain aria was cleared as the first artificial intelligence software to detect, measure and grade amyloid related imaging abnormalities, the serious adverse effect associated with new anti amyloid Alzheimer's therapies. The company describes that clearance as the first computer aided detection and diagnosis solution authorised in neuroradiology, a category held to markedly higher evidence standards than quantification alone. In the United Kingdom the multiple sclerosis measures received a Medtech Innovation Briefing from the national health technology assessment body in April 2022. In the United States the service qualifies for reimbursement under an established procedure code for three dimensional post processing. Ownership changed in 2026: icometrix is now a GE HealthCare company. That is the most consequential fact on this record for a prospective buyer, and it cuts both ways. It brings distribution, capital and an installed scanner base, and it raises the questions any acquisition raises about roadmap independence, pricing under a larger vendor's commercial structure, and whether neutrality toward competing scanner manufacturers persists. The brand, the products and the site remain under their own name, which is why this is indexed as its own record. Founded in 2011 as a spinout in Leuven, Belgium, with United States operations established in the Boston area in 2016 and a further presence in New Jersey. Output is delivered as colour coded segmentations in DICOM format, concise reports carrying normative reference data, and prepopulated reporting templates. Data handling is stated to comply with United States health privacy law and European data protection law, with encrypted transfer, secure storage and controlled access. No pricing of any kind was located, and no customer count, deployment scale figure or named health system reference was found in public material.

Optellum scores whether a lung nodule is cancer. Virtual Nodule Clinic identifies, triages and tracks patients with pulmonary nodules, and its Lung Cancer Prediction AI computes a malignancy risk score from full patterns of three dimensional pixels in an ordinary computed tomography scan, using a convolutional neural network trained as a radiomics based digital biomarker. The clinical problem it addresses is specific and large: roughly two million incidental lung nodules are found each year in the United States and a substantial share of those patients never receive follow up, while small tumours treated early carry survival rates far above late stage disease. The company is building a wider platform, LungOS, on what it describes as the first thorax computed tomography foundation model, announced in August 2025. Regulatory standing spans four jurisdictions. United States clearance came in March 2021, the first for artificial intelligence decision support in lung cancer diagnosis, followed by European medical device regulation marking, United Kingdom marking, and Australian class IIb approval in February 2026. The reimbursement position is what sets this record apart and is rare enough in this index to state plainly. Two temporary procedure codes took effect on 1 July 2022 covering quantitative computed tomography tissue characterisation, one standalone and one alongside a concurrent scan. In the hospital outpatient setting the standalone code sits in a new technology payment classification at a rate of roughly 600 to 700 dollars per use, paid at the full rate without multiple procedure discounting. The company maintains a dedicated reimbursement page explaining the mechanics, and it is candid about the limits: these are temporary category codes carrying no relative value units, claims may need supporting documentation to establish medical necessity, and regional contractors decide case by case. Evidence runs from algorithm validation through to health economics. External validation appeared in Thorax in 2020 and risk stratification accuracy in the American Journal of Respiratory and Critical Care Medicine the same year. Real world impact on time to diagnosis and follow up rates was published in BMJ Open Respiratory Research in December 2025 and presented at a European oncology congress. In March 2026 a Vanderbilt led lifetime payer perspective analysis reported the approach cost effective, and in June 2026 the company was awarded competitive national research funding for a multi site pilot in the English health service. Deployment passed 250 clinical sites and three million scans analysed by June 2026, with named customers including the University of Pennsylvania Health System, Vanderbilt University Medical Center, Oxford University Hospitals and Tanner Health. Security is certified against the 2022 revision of the international information security management standard, achieved in August 2024, with the scope enumerated across design, development, manufacture, sales and support, and the company runs a public trust centre. Headquartered at the Oxford Centre for Innovation with a United States office in the Texas Medical Center in Houston. A Series B closed in January 2026 adding new strategic investors.

Brainomix reads brain and lung scans and tells a clinician what it sees, fast enough to change what happens next. Brainomix 360 Stroke interprets acute stroke imaging in real time, and e-Lung quantifies features on CT lung scans to support earlier identification of interstitial lung disease including idiopathic pulmonary fibrosis, built on a proprietary imaging biomarker the company calls the weighted reticulovascular score. The evidence is what distinguishes this record. A study published in The Lancet Digital Health in December 2025 examined 15,377 patients whose scans were reviewed with the tool from January 2022 onward and found clots identified more than an hour earlier. Separately, real world evaluation has been associated with an increase of more than 50 percent in mechanical thrombectomy rates, and the company describes itself as the only stroke imaging tool with demonstrated impact on treatment rates. That is an outcome claim rather than an accuracy claim, and this index has spent a great deal of effort asking other vendors for exactly that distinction. On the lung side, a study with AstraZeneca published in the American Journal of Respiratory and Critical Care Medicine showed the biomarker stratifying patients at risk of pulmonary fibrosis. Regulatory standing is correspondingly strong. The company holds ten United States clearances, eight covering the stroke platform and two covering e-Lung, alongside European marking, with the first stroke clearance in 2023, the e-Lung clearance in May 2024 and a further stroke clearance in April 2025. The deployment and data architecture deserves attention because it answers questions most vendors leave open. Brainomix 360 runs as a managed appliance on a dedicated physical server, on a virtual server using infrastructure the hospital already owns, or in a secure cloud environment. Patient data is stored on the Brainomix server inside the hospital and is not transmitted externally; email notifications carry pseudonymised results only. Data is encrypted in transit and at rest, retention is configured by the customer, and authentication runs through the hospital's own directory using LDAP, Microsoft Active Directory or single sign on with multi factor authentication. The company is certified against the international information security management standard and publishes a trust centre. Results return to the picture archiving system as annotated DICOM series or PDF reports and are also available through web and mobile applications. An Oxford spinout with offices in the United Kingdom, Ireland and Chicago, led by co founder and chief executive Michalis Papadakis. Deployment covers more than 70 hospitals in the English health service alongside sites in the United States and continental Europe. A Series C reached 18.8 million pounds in February 2026 after a 4.8 million pound extension led by Parkwalk Advisors and Hostplus, with United States investor Modi Ventures joining. One gap stands against all of that: no pricing information of any kind was located, and no commercial terms are published anywhere.

Canary Speech sells the model rather than the application. Its vocal biomarker engine extracts roughly 2,590 acoustic and linguistic features from the human voice every 10 milliseconds and returns behavioral and cognitive indicators from as little as 20 to 45 seconds of natural speech, delivered through an API that other products embed. Canary Ambient is the API first real time offering, running on patient and clinician conversations, with a continuous monitoring variant added subsequently. Canary Cognitive addresses the cognitive side. Because the analysis reads how someone speaks rather than what they say, the company describes the approach as language agnostic and device agnostic, workable on anything with a microphone. The published evidence is unusual in this lane for what it admits. A 2026 paper in the Proceedings of Artificial Intelligence in Medicine reports unweighted average recall of 0.70 for depression and 0.68 for anxiety, with a combined behavioral health assessment reaching sensitivity of 0.76 and specificity of 0.65 on a remote test set, and sensitivity of 0.68 with specificity of 0.80 on an independent in clinic dataset collected by tablet. Those are moderate numbers and the company published them anyway, at an operating point, which almost no competitor here does. The same work introduces an uncertain classification that flags low confidence predictions near the decision boundary rather than forcing a call, which is a deliberate clinical safety choice rather than a performance figure. Markets span health systems, payers, pharmaceutical and clinical trial work, employers, telehealth and clinical call centres. In February 2026 the company partnered with Intermountain Ventures on an institutional review board approved study led by a neurologist at Intermountain Health, testing whether vocal features can identify multiple sclerosis. That same month it entered consumer health through JubileeTV, embedding passive speech analysis into video calls between older adults and their families, its first deployment outside clinical and research settings, producing indicators the company is careful to describe as nondiagnostic. Founded in 2017 in Provo, Utah by Henry O'Connell, who began his career at the National Institutes of Health in a neurological disease group before a long medical device career, and Jeff Adams, a speech recognition specialist. Roughly 22.35 million dollars raised in total, including a 13 million dollar Series A in June 2024 led by Cortes Capital and a further round in January 2025. Investors include Sorenson Communications, SMK Corporation, Plug and Play Japan and Hackensack Meridian Health, the last of which is a health system and therefore a party whose commercial and investment interests a buyer should hold separately when weighing any reference from it. One disclosure detail deserves attention before a security review. The company's security page lists an extensive set of assurance programs covering the SOC family, FedRAMP, FISMA and four ISO standards, and attributes them accurately to the cloud partners it builds on rather than claiming them. Canary's own stated position is HIPAA compliance, control audit against a recognised benchmark, vulnerability scanning and cleared API penetration testing. No attestation held by Canary itself was located. The wording is honest; the visual effect of the list is not, and a buyer skimming it may credit the company with certifications it does not hold.

Aiberry runs a bot administered interview and scores mental health from how a person answers it. A digital animated assistant called Botberry asks open questions, the person replies in their own words on camera, and an ensemble of machine learning models reads text, audio and video together to produce a depression risk score, symptom level insight across mood, concentration and energy, and a transcript of every response. The design intent is to replace the multiple choice self rating form, where a person picks the answer that best describes them, with something closer to a conversation. Two products are sold. Digital MindCare is anonymous and login free, aimed at schools, employers and community health organisations, and collects no account or identified health record. Smart MindCare, also presented as SmartAI MindCare, is the account based clinical product where personal and health information is collected and results land in a portal for a clinician. Four buyer settings are named: corrections, corporate wellness, behavioral health and recovery, and higher education. The evidence position rests on a single peer reviewed study, and it needs reading carefully. The paper appeared in the Journal of Affective Disorders in 2024, led from the University of Texas at Austin with the Georgetown University Medical Center and the University of Arizona, covering nearly 400 participants aged 18 to 74 who completed both a Botberry interview and a gold standard depression questionnaire. It reported performance comparable to the questionnaire and found no evidence of bias by gender, age or race. Press coverage presents this as an independent university validation. The paper's own declarations state that it was funded by Aiberry, that two authors are employed by Aiberry including the author credited with conceptualisation, methodology, formal analysis and supervision, and that a third is an Aiberry research coordinator. The disclosure is properly made in the journal. The framing built around it in press material is what diverges. A second currency problem sits beside it. The company research page still links a preprint rather than the published paper, so a reader following the vendor's own citation lands on the unreviewed version of work that has since cleared review. Based in Seattle and led by co chief executives Linda Chung and Johan Bjorklund with founding scientist Newton Howard. An 8 million dollar seed round led by Confluence Capital Group with Ascension AI participating was announced in March 2023. Named collaborators include Georgetown University, the University of Arizona and Advocate Aurora Health. Disclosure outside the clinical study is thin, and a buyer should expect to establish most of it privately. A dedicated pass located no security page, no external security attestation, no trust center, no named record system integration, no hosting or data residency statement, and no pricing of any kind. The company news feed has not been updated since 2023 and no funding round after the 2023 seed was located, so commercial trajectory cannot be established from public material in either direction.

Videra Health builds multimodal artificial intelligence for behavioral health, analysing what a patient says, how they say it, and how they look while saying it. The platform sends asynchronous video, voice or text check ins that patients complete on their own devices in roughly three minutes, and scores them against established clinical instruments. Three model families work together: language models reading transcribed speech for markers such as absolutist phrasing, word complexity and sentiment; acoustic models extracting speech rate, pause duration, pitch variation and articulation; and computer vision models reading facial movement, microexpressions and affective state. The company publishes a model table, which is rare in this index. Five named models each carry a clinical application, a reported area under the curve and a modality: PHQ-V for depression screening at 0.88 to 0.90, GAD-V for anxiety at 0.90, PCL-V for post traumatic stress at 0.85 to 0.90, EPDS-V for postpartum depression at 0.89, and a video model for tardive dyskinesia at 0.87 to 0.92. The video instruments were developed in house as video native versions of the standard written scales by the co founder and chief clinical officer. The product line spans the patient journey rather than a single moment. Front Door answers inbound patient inquiries around the clock and captures scheduling preferences, Intake Accelerator moves clinical history and paperwork to the patient's own time, Assess runs continuous check ins between and after visits, Sidekick Notes documents sessions ambiently, Group Notes documents group therapy while separating individual patient voices, and Sidekick Elevate returns communication pattern data to clinicians as coaching. A separate life sciences line applies the same capture infrastructure to clinical trials and post market studies. TDScreen, a tardive dyskinesia screening tool, is offered free to any provider or patient. Reported scale is more than 300 facilities and over one million patient interactions, with more than 100,000 notes generated and a claimed 94 percent completion rate on assessments. Named deployments include Discovery Behavioral Health, which launched a platform called Discovery365 in collaboration with Videra and Brigham and Women's Hospital, and work through the Medical Technology Enterprise Consortium aimed at preventing suicide related deaths among service members. Integrations are named with Kipu, Alleva, BestNotes, BlueStep, Zoom, Epic and Cerner. Academic collaborations are stated with the University of Utah and the University of Texas. Founded in 2019 in Orem, Utah. Roughly 8.6 million dollars is disclosed across a 3 million dollar seed in 2021 and a 5.6 million dollar second seed in May 2024, both led by Peterson Ventures, with Mercato Partners, Epic Ventures, Philo Ventures, Rose Park Advisors and OATV also named. Two cautions belong on the face of this record. The company describes itself as FDA registered and displays that phrase as a badge alongside its HIPAA and security marks. Establishment registration and device listing is a filing obligation, not a review of safety or effectiveness, and it is neither clearance nor approval. No clearance was located for any product. Separately, all five peer reviewed publications are authored by company staff, and the named scientific advisor is a co author on both tardive dyskinesia papers, so the evidence base is genuine, published and internally generated rather than independently replicated.

Behavioral health integration infrastructure sold to risk bearing organisations, health systems and health plans rather than to patients. Founded 2016 in Philadelphia by Chris Molaro, chief executive, and Adam Pardes, chief operating officer; one source gives 2017. Funding totals roughly 58 million dollars, including a 20 million dollar Series B led by Magellan Health and a 25 million dollar growth round led by SEMCAP Health, with a later private equity round. The premise is that behavioral health need surfaces in physical care settings that are not equipped to handle it. Three named products address that in sequence. BHIQ applies machine learning across claims, record system, admission discharge transfer and assessment data to surface undiagnosed and undertreated behavioral health conditions across a population. IntegrateBH, expanded in February 2026 with capabilities from recent acquisitions, operationalises those findings through risk identification, intelligent referral matching and care coordination, delivering patient data inside the provider's existing workflow. TxProgress measures treatment impact over time. One component distinguishes this record from every other behavioral health vendor in the index and should be read against them directly. NeuroFlow operates Response Services, a human staffed safety net for crisis moments, which the company describes as unique in its ability and willingness to address suicidality. The two patient facing conversational products built immediately before this one, Wysa and Limbic, publish no crisis escalation pathway at all. This company sells one. Named customers include Magellan Health, Trinity Health, Bozeman Health and the Department of Defense, with platform reach reported at roughly 15 million patients. Note that Magellan Health is both a customer and the investor that led the Series B, so that reference is not disinterested. The company was a finalist in a federal grand challenge to reduce veteran suicide, receiving 250,000 dollars and advancing to a second phase, which is competitive government evaluation rather than a marketing award. Security credentials are stated correctly and with their tiers: health specific certification at the implemented one year level, a controls report at the second type, and privacy compliance. A published example reports a large integrated health system stratifying more than 3,000 patients within four months. The gaps are measurement and commerce. No accuracy figure is published for the risk identification models, no peer reviewed evidence was located, and nothing about pricing is disclosed.

Clinical artificial intelligence for mental health, and the most heavily regulated and best evidenced record in this index. London based, founded 2017 by a team including Dr Ross Harper, chief executive; one source gives 2018. Limbic Access is an artificial intelligence triage and clinical assessment assistant that conducts a conversational psychological assessment supporting patient self referral, classifying the eight common mental health disorders treated by the national talking therapies programme at a stated 93 percent accuracy. In January 2023 it became the first and only artificial intelligence mental health chatbot in the world to obtain Class IIa medical device certification under the United Kingdom conformity regime, audited by a named certification body against clinical effectiveness, safety and risk management. Limbic Care is a clinical assistant for caseload management and continuous patient support, now including ambient scribe functionality that captures conversations, generates letters and standardises notes, carried under a separate Class I device classification. Deployment is at national scale within one health system. Tools have delivered assessments for more than 650,000 patients, across 66 percent of integrated care boards in England and roughly 45 percent of talking therapies services. Funding includes a 14 million dollar Series A led by Khosla Ventures with participation from Gaingels and the family office Illusian, alongside expansion into the United States. The evidence base is unlike anything else in this index. The company reports two publications in a leading peer reviewed medical journal: a 2024 study on improved accessibility for minority groups, and a randomised double blind clinical trial reporting that specialist trained clinical agents delivered cognitive behavioural therapy outperforming both human clinicians and general purpose large language models, built on a cognitive layer architecture the company calls the Limbic Layer that sits above foundation models. Recorded as the company states it. Neither paper was retrieved and read in this pass, and the trial result in particular is an extraordinary claim that a buyer should verify at source before relying on it. Trust disclosure is correspondingly detailed: information security and cyber certification, the national clinical risk management standard for health software manufacturers, a named data protection officer, individually enumerated patient data rights, a breach notification commitment, and a stated practice of stripping patient identifiable information from incoming referral records. One statement deserves particular attention and is treated carefully on the security axis: the company describes itself as compliant with the main information security standard while separately describing a different scheme as a held certification, and compliant is not certified. The gap across this record is commercial. No pricing, unit of charge or licensing basis was located anywhere.

Enterprise patient flow, electronic patient record and clinical decision support software, and the only vendor in this index whose business sits almost entirely outside the United States. Listed on the Australian Securities Exchange, led by managing director and chief executive Kate Quirke. All figures below are Australian dollars. The platform is Miya Precision, positioned as modernising legacy health systems by standardising data across complex environments and delivering real time clinical decision support on top of it. Coverage spans patient flow and bed management, command centre operations, emergency department workflow, care coordination, virtual care and, increasingly, full electronic patient record deployments. In June 2026 the company completed the acquisition of the Kyra flow products from Telstra Health, adding 33 customers of which 31 were new and consolidating its position in the Australian patient flow market. Scale is documented rather than claimed, because listing obliges it. The platform runs across more than 400 hospitals globally, supporting more than 50,000 beds and 130,000 active users. Customers include more than 40 National Health Service trusts in the United Kingdom, healthcare providers in every Australian state and all four regions of New Zealand Health, with stated expansion interest in Canada and the Middle East. Named customers include University Hospitals Sussex, Harrogate, Northumbria, Dartford and Gravesham, Hywel Dda and Western Health, the last of which renewed for a fifth time across twenty years. Financial disclosure is unmatched in this index. Revenue for the year ended 30 June 2026 was 51.6 million dollars, up 27 percent, split 63 percent United Kingdom and 37 percent Australia and New Zealand, with annual recurring revenue of 38.3 million dollars up 34 percent and a record 78.5 million dollars of new and renewal total contract value. Individual contracts are disclosed: the May 2026 University Hospitals Sussex electronic patient record agreement runs seven years at roughly 35 million dollars, extendable to roughly 49 million dollars over ten. The artificial intelligence is a layer rather than the product. The company describes a growing suite of generative capabilities inside Miya Precision covering clinical documentation, summarisation and problem detection, and management frames the platform as one that standardises data and helps deploy algorithms safely in regulated healthcare settings. That framing is close to the infrastructure position this index rejects elsewhere, and the record sits inside the boundary because the company builds its own decision support and generative capability rather than solely carrying other parties' models. The centrality grade reflects the balance. Two record keeping notes. The specific city of headquarters was not confirmed in this pass, and neither was the founding year, so both are omitted rather than guessed. And two axes in this framework, the federal privacy posture and the device regulator status, assume a United States vendor and map poorly here; both are graded against the equivalent obligation in the company's actual markets and the reasoning is recorded on each.

BMJ Best Practice is the fourth of the major subscription clinical reference tools and the one least built on artificial intelligence, which is why it is indexed here with that stated plainly. Published by BMJ Group in London, it gives step by step guidance on symptom evaluation, investigation and treatment, covering more than 90 percent of conditions commonly presenting in hospital across over 30 specialties, with differential diagnosis tables, treatment algorithms, calculators, procedural videos and case reports, updated daily and peer reviewed. Its distinguishing feature is the Comorbidities Manager, which adjusts a treatment plan for a patient's other conditions. The company describes it as the only point of care tool that supports both single condition management and patients with more complex comorbidity, which is a real gap in a category whose topic reviews generally treat one disease at a time. On artificial intelligence it sits behind its competitors and independent comparison says so directly: conversational features are described as a layer rather than the core, and as of 2026 it does not offer a generative question and answer capability comparable to Elsevier's ClinicalKey AI, EBSCO's Dyna AI or Wolters Kluwer's UpToDate Expert AI, all of which reached market between February 2024 and September 2025. A buyer choosing on artificial intelligence maturity should weight that accordingly. What it does have is authority, and one external marker of it is unusually strong. When Google DeepMind researchers built an evaluation of conversational artificial intelligence for disease management, published in Nature in 2026, they selected BMJ Best Practice as one of only two core clinical guideline resources, alongside the United Kingdom's national institute guidance. The ground truth management plans for diagnosis, investigation, treatment and follow up across 100 multi visit patient scenarios were derived from those sources, and both the physicians and the artificial intelligence in the study worked from a corpus of 527 national guidelines and 100 topics from this product. Evidence is graded in evidence based medicine style with links to primary literature and Cochrane reviews. Access is free to National Health Service staff in England, Scotland and Wales, and paid elsewhere, usually through institutional subscription.

Ultromics reads a routine echocardiogram and detects disease a human reader cannot see in it. A spin out from the University of Oxford founded in 2017 by chief executive Ross Upton, working with Professor Paul Leeson at Oxford and Dr Patricia Pellikka at Mayo Clinic, it has built the EchoGo platform around the idea of identifying disease directly rather than automating measurements. It holds four Food and Drug Administration clearances and two Breakthrough Device designations. EchoGo Heart Failure, cleared in 2022, aids detection of heart failure with preserved ejection fraction, a common form that is difficult to diagnose. EchoGo Amyloidosis, cleared in November 2024 under K240860, was the first artificial intelligence tool cleared for cardiac amyloidosis and the first device enrolled in the agency's Total Product Lifecycle Advisory Programme to reach marketing authorisation, from a pilot of fifteen breakthrough cardiovascular devices. The amyloidosis model works from a single routinely acquired apical four chamber videoclip, which is materially less input than the usual pathway of electrocardiography, cardiac magnetic resonance, scintigraphy and sometimes biopsy. A multi centre international study in the European Heart Journal, run with Mayo Clinic and investigators at the University of Chicago Medicine across more than 18 sites, validated it on an external cohort of 2,719 patients with a 22 percent prevalence of the disease, reporting an area under the curve of 0.93 with 85 percent sensitivity and 93 percent specificity across more than 12,500 cases. The company separately publishes the figures from clinical evaluation under intended use conditions in its clearance summary, 84.5 percent sensitivity and 89.7 percent specificity. Performance held across all major subtypes and distinguished the disease from conditions that mimic it, including hypertensive heart disease, heart failure with preserved ejection fraction and hypertrophic cardiomyopathy. Evidence for the heart failure product is cited in the 2025 American Society of Echocardiography diastology guidelines, and that product carries Medicare reimbursement through Category III code 0932T for outpatient use and a new technology add on payment for inpatient settings. More than 25 peer reviewed studies support the platform, which is in use at UChicago Medicine, Northwestern and City of Hope. Development collaborators include Pfizer and Janssen Biotech.

Veracyte builds genomic classifiers, each one a machine learned model over RNA whole transcriptome data, and it is the direct competitor to the artificial intelligence prostate tests indexed alongside it. Founded in 2008, based in South San Francisco with laboratories there and in San Diego, and listed on Nasdaq, it reported testing revenue of 135.8 million dollars in the fourth quarter of 2025, up 21 percent. The flagship is Decipher Prostate, a 22 gene classifier developed with machine learning that estimates the risk of metastasis and informs how intensively to treat. More than 300,000 patients have been tested since launch, over 100,000 of them in 2025 alone, with roughly 27,200 tests in the fourth quarter and a fifteenth consecutive quarter of volume growth above 20 percent. Market penetration is stated at about 33 percent. Its standing in the guidelines is the strongest of any record in this index. Decipher Prostate is the only gene expression test to reach Level I evidence and inclusion in the risk stratification table of the National Comprehensive Cancer Network guidelines for prostate cancer, and those guidelines uniquely recommend using its score to guide whether to add hormone therapy after prostatectomy, a recommendation resting on a Phase 3 randomised trial with a median of twelve years of follow up. Performance has been examined in more than 85 studies covering over 200,000 patients, and results have been linked to real world outcomes through the National Cancer Institute's SEER database. The classifier is currently under investigation in seven Phase 3 randomised trials sponsored by that institute. The wider portfolio uses the same method in other cancers: Decipher Bladder, a 219 gene classifier sorting tumours into five molecular subtypes; Afirma for thyroid nodules; Prosigna for breast cancer; Percepta for lung, including a nasal swab test whose trial has completed enrolment; and Envisia for interstitial lung disease, with lymphoma and renal tests in development alongside a tumour informed test for minimal residual disease. The company holds exclusive global access to the nCounter analysis system, which lets laboratories run its tests locally rather than shipping samples. HalioDx is among the businesses it has absorbed.

Medalogix builds predictive models for home health and hospice and nothing else. Based in Nashville and founded around 2014, led by president and chief executive Elliott Wood, it is described in trade coverage as the only data science company focused entirely on this sector. It absorbed the hospice analytics firm Muse Healthcare in a merger, and Muse now trades as a Medalogix product rather than independently. Three products sit on the same approach. Care predicts hospitalisation risk in home health and recommends a plan built from the histories of similar patients. Pulse, launched in 2022, works visit by visit, drawing on assessment data, vital signs, medications and clinician narrative notes to show how a patient's risk is moving after each contact; the company reports it draws on more than 11 million home health visits and performs over 300 billion calculations. Muse addresses hospice, comparing more than 800 data points for a patient against millions of records to identify when someone is entering their final days. What Muse predicts is death. The company and its customers state precision above 90 percent for identifying a patient within seven days of dying, and the purpose is to send more people rather than fewer: agencies use the prediction to raise visit frequency from nurses and social workers at the end of life, with customers reporting as many as 82 percent more visits than the national average during that period. Reported outcomes elsewhere are operational. The home health product is associated with a near 20 percent reduction in hospitalisation and 2.4 fewer visits per episode, and a separate partner reports an 8.2 percent reduction in unwanted hospital transfers. Distribution runs through the sector's dominant record systems rather than direct. Homecare Homebase, which states it serves around 37 percent of United States home health and hospice providers, is an exclusive partner for the hospice product, and the models also surface inside the MatrixCare workflow. Enterprise customers include Amedisys, Enhabit and AccentCare.

Doximity Ask is a free artificial intelligence clinical reference tool for physicians, and it is where Pathway ended up. Anyone searching for Pathway or Pathway Medical Inc is looking at this product. Doximity, founded in 2010 and listed in New York, states its network reaches more than 80 percent of United States physicians. Its chief executive Jeff Tangney previously co founded Epocrates, the first medical reference application on the iPhone. In July 2025 it acquired Pathway Medical Inc, a Montreal company, for 26 million dollars in cash plus up to 37 million in equity grants, around 63 million in total. Pathway's own site now states it has joined Doximity and its capabilities live on in Doximity Ask. Pathway was a small team, roughly six people with physicians making up half, that emerged from a postdoctoral programme at the Mila artificial intelligence institute and was founded years before general purpose language models arrived. Over seven years it assembled what the acquirer describes as one of the largest structured datasets in medicine built for artificial intelligence, covering close to every guideline, drug and landmark trial across the major specialties. Its model scored a reported 96 percent on the United States Medical Licensing Examination benchmark, and an earlier version was reported to score above 97 percent of human test takers, exceeding Google's flagship medical model on that benchmark. Co founder and chief medical officer Louis Mullie and chief executive Jon Hershon led the company. The product answers clinical questions with concise, evidence based guidance referenced to peer reviewed literature rather than returning long form articles. Under Pathway it reached more than a million registered healthcare professionals across 180 countries and 33 specialties, with a premium tier at 300 dollars a year that thousands paid. Under Doximity it is free, delivered inside a platform clinicians already use. Pathway's own guidance was that the assistant is a supplemental resource whose outputs should be independently verified. This record covers the clinical reference product. Doximity GPT, the writing and administrative assistant, is indexed separately.

Mirvie predicts complications of pregnancy months before they appear, from a blood sample rather than from the mother's characteristics. Founded in 2018 and based in South San Francisco, it combines cell free RNA transcriptomics with machine learning to read the molecular state of the placenta and the developing pregnancy. The first commercial product, Encompass, launched in 2025 and predicts preeclampsia risk between 17.5 and 22 weeks of gestation in pregnancies carrying no pre existing high risk conditions. The company reports that a low risk result carries a 99.7 percent probability of not developing preterm preeclampsia, and that the test identified 91 percent of pregnancies that went on to develop preterm preeclampsia among women aged 35 and over without pre existing risk factors. Encompass is sold as a package rather than a result alone, pairing the test with a preventive action plan and a virtual assistant. The evidence base is unusually substantial for a company of this age. A validation study published in Nature Communications in April 2025 drew on more than 9,000 pregnancies from the company sponsored multi centre Miracle of Life prospective study, identifying RNA signatures that distinguish severe from mild hypertensive disorders of pregnancy. Earlier work appeared in Nature on preeclampsia prediction and in the American Journal of Obstetrics and Gynecology on preterm birth, with research on fetal growth restriction presented at a maternal fetal medicine meeting. The platform is stated to have examined the molecular health of close to 11,000 pregnancies. The clinical argument is that existing practice identifies risk from maternal characteristics under national guidelines, that preeclampsia rates have nearly doubled in a decade to affect roughly one pregnancy in twelve, and that a molecular signal separates the pregnancies genuinely at risk from those merely resembling them. Where risk is identified, the established response is low dose aspirin and closer monitoring. The company's vice president of clinical development, Thomas McElrath, is a maternal fetal medicine physician at Brigham and Women's Hospital.

Mednition sells KATE, a clinical artificial intelligence platform that works at the emergency department triage desk rather than on the ward. It was founded in 2014, is based in Burlingame, California, and is led by chief executive Steven Reilly. KATE reads the structured intake data a triage nurse enters together with the free text of their notes, compares it against patterns from millions of prior visits, and recommends an acuity level in real time. The company is explicit that it supports rather than replaces the nurse's judgement, adds no new screens and requires no workflow change, which matters because triage is the most time pressured decision point in the hospital. The sepsis model is the flagship. KATE Sepsis received Breakthrough Device Designation from the Food and Drug Administration in November 2023 for detecting sepsis at triage, before any laboratory result exists. It has not been cleared: the designation expedites review rather than authorising marketing, and no clearance was located nearly three years later. Performance is published in preprints with named authors rather than only in marketing copy. An early model reported an area under the curve of 94 percent for sepsis detection at triage. A later model reported an area under the curve of 99 percent with 95 percent sensitivity and 96 percent specificity, on a retrospective cohort of 540,884 patients containing 14,676 positive sepsis cases across 16 hospital sites, using the current academic sepsis definition. Against standard screening protocols the company reports sensitivity improvements of 74 percent for sepsis, 80 percent for severe sepsis and 118 percent for septic shock. The company names the failure mode its category is known for rather than avoiding it, stating that the challenge has been achieving high sensitivity without a collapse in specificity and the false positive rates and alert fatigue that follow. Adventist Health has adopted KATE systemwide, and named sites include Shady Grove, White Oak, Fort Washington and Glendale. A separate paediatric safety product covers the same triage moment. KATE was named best in show in the capacity crisis category at the 2025 HIMSS conference.

Cerebriu is a Copenhagen company founded in 2018 whose Apollo suite intervenes in a brain MRI examination while it is still happening, rather than analysing the images afterwards. Its founders are Akshay Pai, Robert Lauritzen, who is chief executive, Erik Dam, Mads Nielsen and Martin Lillholm. The suite has three parts. Smart Protocol analyses an abbreviated opening set of images and suggests which sequences should be acquired next, so the protocol adapts to what has already been seen while the patient is still on the table. Smart Alert detects critical findings during the examination itself, naming infarcts, intracranial haemorrhage and tumours, so a scan can be escalated before the patient leaves. Smart Reading prioritises high risk studies in the reporting queue. Regulatory position is European rather than American. Apollo is CE marked under both the older medical device directive and the current regulation, while clearance from the Food and Drug Administration is described as pending and the United States status is stated as for investigation only. In 2025 the company secured CE marking for Apollo Smart Protocol as a product embedded inside a Siemens Healthineers MRI system, which it describes as the first clearance anywhere for clinical artificial intelligence embedded in a scanner from a major manufacturer. A validation study published in the European Journal of Radiology in September 2023 reported roughly 89 percent sensitivity and 90 percent specificity for ischaemic lesions across 800 patient scans. Deployments span hospitals in Denmark, the United States, Norway, India, Israel and Brazil. Funding totals about 26.8 million euros, comprising a 17.4 million euro Series A in 2024 led by North Ventures with EIFO, Denmark's export and investment fund, and Sagitta Ventures, followed by 9.4 million euros in 2025. The company also distributes through two independent radiology artificial intelligence platforms, CARPL and deepc, alongside its manufacturer route.

RapidAI analyses brain and vascular imaging in real time to support urgent treatment decisions, principally in stroke. It was founded in 2012, is based in San Mateo, California, employs between 200 and 500 people and is led by chief executive Karim Karti. Its platform is used in more than 2,500 hospitals across over 100 countries and the company states more than 700 clinical studies stand behind it. The evidence position is the defining feature. Research using its perfusion software contributed to the trials that extended the treatment window in national stroke guidelines, and the company states it holds the only perfusion software with a Food and Drug Administration indication for selecting patients for mechanical thrombectomy, and the only software shown to predict subsequent infarct volume from initial perfusion imaging. It presented 28 scientific abstracts at the International Stroke Conference in February 2026 spanning aneurysm monitoring, ischaemic stroke detection, imaging visualisation and radiology workflow. The product is organised as modules on the Rapid Enterprise Platform, delivered through Rapid Edge Cloud and a radiology application called Rapid Navigator Pro, integrating with existing image archiving and record systems. In November 2025 five further modules were cleared: DeltaFuse, which aligns serial head scans to reveal subtle change and is reported to cut radiologist comparison time by over 36 percent, LMVO for vessel coverage on angiography, MLS for quantifying midline shift, OH for suspected obstructive hydrocephalus, and Aortic for measurement. The company frames this as moving beyond triage notification into characterisation, quantification and tracking change over time. It has raised 75 million dollars, most recently in July 2023, holds a working relationship with Amazon Web Services, and has a partnership with Saudi Arabia's largest healthcare provider covering 20 health clusters.

Eyenuk makes EyeArt, an autonomous artificial intelligence system that grades a retinal photograph and returns a diagnosis without any clinician reading the image. It is one of only three systems cleared by the Food and Drug Administration for diabetic retinopathy testing in the United States, alongside the already indexed Digital Diagnostics and AEYE Health, and it is based in Los Angeles. Clearance came in August 2020 under 510(k) K200667. EyeArt was the first cleared autonomous system able to detect both more than mild and vision threatening diabetic retinopathy in a single test, and the first to return a diagnostic output for each eye separately. Its indication is confined to adults with diabetes not previously diagnosed with more than mild disease. Results are returned at the point of care in under a minute. Two further regulatory positions distinguish it. Version 2.2.0 added clearance for the Topcon NW400 camera alongside two Canon models, making it the only cleared system usable with retinal cameras from different manufacturers rather than tied to one. And version 3.0 holds Class IIb CE marking under the European medical device regulation for three diseases from one exam: diabetic retinopathy including macular edema, age related macular degeneration, and glaucomatous optic nerve damage. It is the only system holding both that three disease European marking and United States clearance for diabetic retinopathy. It is also approved by Health Canada. In September 2025 Norway selected EyeArt as the artificial intelligence system for its national diabetic eye screening programme, following a competitive procurement run by the national purchasing organisation on behalf of the regional health authorities. Grading runs in under 30 seconds with no ophthalmologist review and results are written into the national electronic record. Other deployments include more than 25 centres in Italy, a German diabetes clinic, rural and remote screening in Canada, and Federally Qualified Health Centres in Delaware through a partnership with the American Academy of Ophthalmology. Founded by Kaushal Solanki; chief executive Gaurav Agarwal as of 2025.

Prenosis is a Chicago company whose Sepsis ImmunoScore was the first artificial intelligence diagnostic for sepsis ever granted marketing authorisation by the Food and Drug Administration, cleared through the De Novo pathway on 3 April 2024. Because De Novo creates a new device classification, that authorisation established the category a later competitor used as its predicate, which makes this record the regulatory origin point of the whole cleared sepsis segment. What separates it technically from the other deterioration products in this index is that it does not read the record alone. The score combines biological markers measured from a blood sample with clinical data drawn from the record, using up to 22 parameters, and returns a risk score placing the patient in one of four discrete risk categories. Those categories are tied to length of stay, in hospital mortality and escalation of care within 24 hours, meaning intensive care admission, mechanical ventilation or vasopressor use. The company states explicitly that it is not an alert system. The underlying asset is the Immunix platform and the biobank built on it: more than 100,000 blood samples from over 25,000 patients, assembled across a decade with ten partner hospitals and held in a biosafety level 2 laboratory in Chicago, paired with clinical data from those hospitals' records. The company describes this as the largest combined biological and clinical dataset in the world for acute care patients suspected of serious infection. Sepsis ImmunoScore is distributed commercially through a collaboration with Roche, appearing on the navify Algorithm Suite. In January 2026 the company announced 40 million dollars, comprising a 20 million dollar Series A led by PACE Healthcare Capital with the Labcorp Venture Fund and Carle Health, and a 20 million dollar federal contract from BARDA funding a randomised controlled trial of 800 patients with severe respiratory infections. Co founder and chief executive Bobby Reddy Jr. Founding year was not confirmed in this pass and is left blank.

Fabric sells what it calls a care enablement system: conversational intake, triage, routing and treatment across virtual and in person care, sold to health systems, health plans and employers. It was founded in 2021 as Florence by Aniq Rahman, is based in New York, and rebranded to Fabric in 2024. The company is a roll up and that is the most important thing to understand about it. Five acquisitions in under three years assembled the product: Zipnosis, an asynchronous virtual care platform bought from Bright Health in April 2023; GYANT, a conversational artificial intelligence and patient engagement company bought all cash in January 2024, whose co founder Stefan Behrens became chief strategy officer; MeMD, bought from Walmart, which added payer and employer distribution; and TeamHealth Virtual Care, which added a 50 state clinical network of actual clinicians. Anyone searching for Zipnosis, GYANT or MeMD as separate companies is looking at brands that now sit inside this one. The product is organised as three suites covering in person care, virtual care and engagement, with a layer the company calls Hybrid AI that pairs conversational models with physician authored clinical logic and customisable decision trees. It raised a 60 million dollar Series A in February 2024 led by General Catalyst with Thrive Capital, GV, Salesforce Ventures, Vast Ventures, BoxGroup and Atento Capital, taking total funding past 80 million dollars. Named customers include OSF HealthCare, MUSC Health, Luminis Health and Intermountain, and the company stated coverage of more than 100 million lives as of September 2024. Published operational claims include clinicians working two to ten times faster depending on setting and a 15 percent reduction in call centre volume, and the acquired asynchronous product was described as automating 99 percent of administrative work with low acuity conditions treated in 89 seconds.

Anumana builds neural network algorithms that read a standard electrocardiogram to detect disease the trace was never thought to reveal. It was founded in 2021 as a joint venture between Mayo Clinic and the health data company nference, and is based in Cambridge, Massachusetts. Mayo Clinic co founded the company and holds a financial interest in it, which the company discloses in its announcements. The premise is that an inexpensive, century old test contains signal beyond what a human reader can extract. The platform is stated to be built on more than 22 million patient records spanning more than 20 years, obtained through exclusive partnerships with academic medical centres, and the flagship low ejection fraction model is described as developed from roughly 2.9 million paired electrocardiogram and echocardiogram studies across more than 676,000 patients. Three algorithms have been cleared by the Food and Drug Administration. ECG-AI LEF detects low ejection fraction and is available in the United States and the European Union. ECG-AI PH, cleared March 2026, detects pulmonary hypertension and is the first such algorithm cleared for use with a standard 12 lead electrocardiogram. A cardiac amyloidosis algorithm was cleared in April 2026 and is stated to be the first and only one for that indication on a standard 12 lead trace. Several algorithms have held Breakthrough Device Designation, and the amyloidosis product was selected among the first 15 devices in the agency's Total Product Life Cycle Advisory Programme pilot. The company raised a 25.7 million dollar Series A led by its two founders alongside Matrix Capital Management, Matrix Partners and NTTVC, and acquired the cardiac electrophysiology data company NeuTrace in 2022. It runs algorithm development collaborations with Novartis, Johnson and Johnson and Pfizer.

Guardant Health is a publicly traded precision oncology company, listed on Nasdaq as GH, founded in 2012 and based in Palo Alto, California, led by co chief executives Helmy Eltoukhy and AmirAli Talasaz. It sells blood and tissue based cancer tests across four stages of care: screening the average risk population, selecting therapy in advanced disease, monitoring for recurrence, and supporting biopharmaceutical development. The therapy selection product is Guardant360. Guardant360 CDx was the first liquid biopsy approved by the Food and Drug Administration for comprehensive genomic profiling, and by January 2026 carried its 25th companion diagnostic indication. In May 2026 the agency approved Guardant360 Liquid CDx, which the company describes as the largest approved liquid biopsy panel, assessing a genomic footprint 100 times wider than the previous version and combining genomic with epigenomic profiling from a single blood draw. The seven companion diagnostic indications on the earlier test transferred to it. A separate Guardant360 Tissue product is a laboratory developed test run under the company's own clinical laboratory certification and is not approved by the agency, which is a distinction buyers frequently miss. The screening product is Shield, approved in July 2024 as the first blood test for primary colorectal cancer screening. It was granted Advanced Diagnostic Laboratory Test status by the Centers for Medicare and Medicaid Services, was added to the American Cancer Society colorectal cancer screening guidelines in 2026, and received a UnitedHealth Group coverage decision effective 1 August 2026. Guardant Reveal covers recurrence monitoring and holds Medicare coverage for colorectal cancer through the molecular diagnostics services programme. The company describes an artificial intelligence layer called the Smart Platform, and a branded capability called InfinityAI. More than one million blood tests have been performed.

Infermedica, founded in Poland and operating globally, builds clinical triage that guides a patient from an undifferentiated symptom to the right level of care. Its Medical Guidance Platform runs a dynamic question and answer flow over a physician curated knowledge base covering roughly a thousand symptoms and conditions, with a probabilistic inference engine that computes likely causes and urgency and chooses which question to ask next, and a continuous validation process testing the engine against clinical vignettes. Machine learning is used in controlled feedback loops to refine the model against real clinical outcomes rather than as the primary reasoning mechanism, a characterisation that appears both in the company's own material and in the integration documentation of a major cloud partner. Modules cover patient self triage, pre visit intake, and a nurse triage assistant, delivered through modular interfaces that embed into websites, applications, portals and call centres in 24 languages. The regulatory position is the strongest in its segment: the platform is certified as a Class IIb medical device under the European medical device regulation and registered with the United Kingdom regulator, alongside ISO 13485:2016 for medical device quality management, ISO 27001:2022 for information security, and a SOC 2 Type 2 report. It states deployments with ministries of health and national health systems across more than 30 countries.

TransformativeMed, based in Seattle under chief executive David Stone, sells the CORES Platform, a set of clinician facing workspaces embedded directly inside the electronic health record rather than sitting alongside it. Built originally on the Cerner MPages extension toolkit and now positioned around Oracle Health Millennium, with Epic deployments as well, it reorganises record data into specialty specific views: patient lists carrying each patient's current issues, rounding preparation, handoff, discharge readiness, task synchronisation across the care team, alert routing through Core Notify, secure messaging, and disease specific modules including one for diabetes. A mobile companion both reads from and writes back to the record. Its own founding description of the method is a proprietary knowledge engineering process, which is configuration and clinical content design rather than a learned model, and the artificial intelligence in the current product arrives largely through a September 2025 partnership with Vantiq, whose real time platform supplies the agentic capability. The company was named 2026 Best in KLAS winner for clinician digital workflow, and has run at University of Washington Medicine, University of Alabama at Birmingham, University of Tennessee Medical Center, a Veterans Affairs division and King Faisal Specialist Hospital.

Azra AI, founded in 2022 in Tennessee by Chris Cashwell and formerly named Invenero Holdings, reads pathology and radiology reports the moment they land in the record and turns what it finds into work that someone is accountable for. Natural language models, stated to be trained on more than 100 million pathology and radiology reports through an exclusive partnership with a large United States health system, identify and classify newly diagnosed cancers and flag suspicious incidental findings, and the platform then carries the patient through navigation, tumour board case management, registry abstraction in the national reporting format, and service line analytics. In April 2026 the company acquired Thynk Health, which specialised in lung cancer screening and incidental findings management, and states that the combined platforms run at hundreds of hospitals including five of the ten largest United States health systems, processing well over half a billion clinical reports and messages a year in real time. A clinical research platform launched in May 2026 extends the same report ingestion into trial matching and into cardiology and neurology. The distinction that places this record here rather than in a diagnostic category is that for its original oncology use the pathologist has already made the diagnosis; Azra finds the patient, not the cancer.

Oatmeal Health, founded in May 2022 by Jonathan Govette and based in San Jose, sells lung cancer screening programmes to federally qualified health centres and health plans, serving Medicaid and Medicare populations that screening programmes have historically reached least. Its platform, ICARE, runs four stages: a model over structured and unstructured record data that identifies patients meeting the recommended screening criteria, automated scheduling and reminders, a vision transformer that detects and risk stratifies pulmonary nodules on low dose computed tomography, and a language model chatbot that explains results and next steps in the patient's preferred language and books follow up. A clinical team works alongside the software. Integration is deliberately broad, with named record systems and any imaging vendor or archive. Two things a reader should hold together: the company builds its own models rather than deploying somebody else's, which distinguishes it from several peers in this index, and its own site states that the diagnostic component is not yet cleared by the FDA, with clearance expected in early 2027, while press from early 2025 described a reimbursable Medicare covered diagnostic tool.

Lucem Health, founded with Mayo Clinic and based in Davidson, North Carolina under founding chief executive Sean Cassidy, sells early disease detection programmes to health systems under the Reveal name, covering lung cancer, colorectal cancer, liver disease, arrhythmias, type 1 diabetes, progression from prediabetes to diabetes, and lower gastrointestinal disorders. Each programme runs a predictive model over electronic health record data a health system already holds, surfaces patients at elevated risk, and wraps that output in outreach, scheduling and care management so the identified patient actually reaches a test. The company does not build the models. Three of the Reveal programmes were developed with Medial EarlySign, an Israeli company whose algorithmic models are its own product, and the arrhythmia programme runs with iRhythm. Lucem describes its own contribution as operationalising clinical artificial intelligence, a discipline it calls AI SolutionOps, and its founding chief executive has framed the company's problem plainly as integrating tools that already work into workflows that do not accommodate them. It raised a 7.7 million dollar round in May 2023 led by Mayo Clinic, Granger Management and Mercy.

Carna Health is a Boston company founded in 2021 by Dr Salvatore Viscomi that builds screening and care management infrastructure for chronic kidney disease and the wider cardio kidney metabolic cluster. It pairs point of care testing with a platform that ingests results from laboratories, testing devices, record systems and health information exchanges, stages patients, and routes those who need specialist care into a referral pathway. Its distinguishing characteristic is where it operates: national and regional screening programmes in Bermuda, Cameroon, the Philippines, the Dominican Republic and Turkey, with entry into the United States market underway, which is an unusual geographic footprint for any vendor in this index. A strategic agreement with Siemens Healthineers supplies point of care testing systems in emerging markets, and a May 2026 partnership brings the Renalytix kidney risk test onto the platform. The company markets itself as artificial intelligence driven throughout, and the record below records what was actually located behind that description.

Creyos, formerly Cambridge Brain Sciences and briefly Creyos Health, is a Toronto company selling digital cognitive and behavioural health assessment to clinicians. The platform administers twelve short computerised tasks, each mapped to a named cognitive domain covering memory, attention, reasoning, verbal ability and planning, and scores a patient against a normative database the company states holds more than 85,000 participants. Digitised versions of standard behavioural health questionnaires sit alongside the tasks, and a care planning capability was added in 2026. The tasks originate in three decades of research by Professor Adrian Owen, who built them at Cambridge, launched the web platform in 2009, and continued validating them at Western University's Brain and Mind Institute after winning a Canada Excellence Research Chair. The company states the tasks have been used in more than 400 peer reviewed studies. Applications span mild cognitive impairment and dementia screening, attention deficit assessment, concussion and traumatic brain injury, and research use. The company positions the product as a clinical decision support tool and describes the platform as registered with the FDA, which is a different and much weaker status than clearance.

Carenostics was founded by Dr Bharat Rao and his son Kanishka Rao after a family member died of chronic kidney disease that had never been diagnosed. The platform reads existing electronic health record data, both structured fields and unstructured notes, to identify patients who already have a chronic disease but carry no diagnosis, and prompts the clinician inside the workflow they are already using. Chronic kidney disease is the lead indication, with asthma, chronic obstructive pulmonary disease and heart failure named as expansion areas, and a separate line of work supporting transplant centre operations. Two design choices define it. Models are tailored to each health system on that system's own local data rather than shipped as one general model, and the company states that no patient information ever leaves the hospital system. Bharat Rao's background is unusually deep for a startup founder in this category: he led artificial intelligence and health analytics at Siemens Healthcare, ran healthcare data and analytics practices at Deloitte and KPMG, and holds the ACM SIGKDD Service Award. The main deployment on the public record is Hackensack Meridian Health, New Jersey's largest health network, which has confirmed the partnership through its own newsroom and shared a Bio-IT World Innovative Practice Award with the company for the kidney disease work.

C the Signs is a London company founded by two NHS general practitioners, Dr Bhavagaya Bakshi and Dr Miles Payling, that identifies patients at risk of cancer during a primary care consultation. The platform combines electronic health record data with patient reported information to assess risk across more than 100 cancer types, predict likely tumour origin, and route the patient into an urgent, test first or non urgent pathway, with the vendor stating a result inside 60 seconds drawn from over 1,000 evidence based data points mapped against NICE and local guidance. It is integrated with EMIS, SystmOne and Vision, which between them cover essentially the whole English general practice software market, and assessments and safety netting are coded back into the patient record. It reports deployment across roughly 1,400 general practices and more than 70,000 patients with cancer detected. It is registered with the MHRA as a Class I medical device, which is a self certified classification rather than a reviewed authorisation, and the company has published its evaluations largely through oncology conference abstracts at ESMO, ASCO, AACR and an NCI symposium.

FDB, formerly First Databank, supplies the drug knowledge that sits underneath most medication decisions made in the United States. Its databases are embedded in the majority of hospitals, physician practices and pharmacies, which means that when a prescribing system warns about an interaction, a dose ceiling or a contraindication, the content behind that warning is frequently this company's rather than the software vendor's. It is owned by Hearst and part of Hearst Health, the same group that owns Zynx Health. The asset is human curated: clinical editors maintain the content and the company's stated credibility rests on rigorous quality control and clinical expertise rather than on any model. What makes the record current rather than historical is a deliberate repositioning. In October 2025 FDB opened pilot integrations of a Model Context Protocol server, and on 31 March 2026 it made FDB MedProof MCP generally available, described as the first such server purpose built for agent driven medication decision support, letting other companies' AI agents and language models query clinically validated drug knowledge in patient specific context rather than answering from training data. Two further products were previewed in March 2026 and are not yet generally available: FDB Script Agent, which turns a spoken clinical conversation into a structured prescription for physician review, and FDB VerifyAssist, an inpatient pharmacy order verification assistant. The company is positioning itself as the grounding layer beneath other people's medication AI.

Genomind pairs a pharmacogenetic laboratory test with software that turns the result into prescribing guidance, with a centre of gravity in mental health. The assay is a clinician ordered buccal swab genotyped by quantitative PCR across 26 to 27 genes, split between 11 that affect how a drug is metabolised and 15 that affect how the body responds to it, returning results in three to five days and covering more than 700 medications. The Precision Health Platform then integrates that genotype with the patient's current medication list to flag gene to drug and drug to drug interactions and propose alternatives. The company was founded in King of Prussia, Pennsylvania by Dr Ronald Dozoretz, sells to individuals, prescribers, long term care facilities, accountable care organisations, employers and health plans, holds a contract supporting testing for United States active duty service members, and licenses its reporting suite to other laboratories so that its interpretation layer reaches patients whose samples it never handled. What distinguishes this record from everything else in the category is what the company did with its own evidence. It sponsored and co authored a multicentre randomised controlled trial of its own test in major depressive disorder, blinded to both participants and raters, and the trial found no significant improvement in the primary efficacy outcome. The company continues to cite that trial on its own science page.

TheraDoc is one of the two incumbent clinical surveillance platforms in American hospitals, monitoring live feeds of laboratory, microbiology, pharmacy and admission data to raise alerts for infection prevention, antimicrobial stewardship, pharmacy surveillance and anticoagulation management, and to produce mandatory infection reporting. It was founded in Salt Lake City in 1999, sold to Hospira, and acquired by Premier in 2014 for 117 million dollars in cash, and is now marketed as Premier's Clinical Surveillance powered by TheraDoc. Its mechanism is a rules engine rather than a model: pharmacists and infection preventionists author their own alert logic, and the system evaluates those written queries continuously against incoming data. Premier makes no artificial intelligence claim for the product on its own pages, which is more accurate than what its principal competitor publishes. The one genuine model inside the product is licensed rather than built, since Premier's own material states that the integrated precision vancomycin dosing tool in TheraDoc comes from InsightRX. Two ownership facts matter for procurement. Premier completed a 2.6 billion dollar take private by Patient Square Capital, having reported an eleven percent revenue decline in fiscal 2025 and having explored strategic alternatives since 2023. And Premier separately owns Stanson Health, so a single supplier now sits behind both this surveillance platform and a point of care decision support product.

VigiLanz is the other incumbent clinical surveillance platform in American hospitals and the direct competitor to TheraDoc. Founded in Minneapolis in 2001 by David Goldsteen and acquired by Inovalon in February 2024, it ingests near real time data from any electronic health record, including admission feeds, medication administration records, laboratory results, microbiology and medication history, across acute, post acute and ambulatory settings, and converts it into alerts for pharmacy surveillance, infection prevention, antimicrobial stewardship, quality improvement and patient safety event reporting. The mechanism is configurable rules, and the company's own product page describes it in a phrase worth quoting because it captures the entire problem this index exists to address: flexible, rule based AI. Independent product listings describe the same engine plainly as configurable and rule based. Alerts surface directly inside the electronic health record for faster acknowledgement, and the platform supports intravenous to oral conversion review, antibiogram trending and transitions of care. As with its competitor, the genuine model reaching clinicians through the product is licensed from elsewhere: Inovalon announced a collaboration in October 2025 bringing DoseMeRx precision dosing into these pharmacy surveillance workflows. The company cites eight consecutive years as the leading pharmacy surveillance solution in one analyst firm's rankings.

EndoTool is an FDA cleared inpatient insulin dosing system and the closest direct competitor to Glucommander, covering intravenous dosing, the transition from intravenous to subcutaneous insulin, and subcutaneous dosing for adult and paediatric patients. Its algorithm is described in independent literature as model predictive control using a nonlinear dosing equation that is individualised and then optimised over time against how that patient's glucose actually responded to the doses already given, drawing on diabetes type, height, weight, creatinine, age and sex. That is a more elaborate approach than the proportional control its main competitor uses, and it is still deterministic mathematics rather than machine learning: the same peer reviewed reviews that describe it group it with rule based systems carrying static decision rules rather than adaptive learning models. The product was built by Monarch Medical Technologies of Charlotte, North Carolina, which also appears in the clinical literature under the name MD Scientific LLC, and Glooko acquired Monarch in September 2025 to extend its outpatient diabetes platform into the hospital, describing the combination as a hospital to home continuum. Glooko has stated that both product lines continue to operate and be supported. The vendor characterises the algorithm as patented and patient specific, and markets against the same 2026 CMS glycemic quality measures its competitor does.

Glytec's Glucommander is the oldest and most widely deployed computerised insulin dosing system in American hospitals, cleared by the Food and Drug Administration as Class II software for intravenous insulin since 2006 and for subcutaneous insulin since 2010, in patients aged two and over. It sits inside the GlytecOne platform alongside surveillance, alerting and analytics, and the company reports use across more than 400 hospitals and health systems and dosing for over 867,000 patients since 2015. The mechanism deserves stating plainly because the marketing does not. Independent reviews describe Glucommander as a proportional integral derivative controller applying a linear formula derived from a 1982 paper, where the hourly insulin dose is a function of blood glucose above a floor multiplied by a factor that steps up until glucose reaches target. It adapts to the individual patient through that feedback loop, which is real closed loop control and is not machine learning, and the same reviews explicitly classify these systems as rule based with static decision rules rather than adaptive learning models. The company nonetheless describes the algorithm as learning each patient's insulin sensitivity. Distribution now extends to the bedside device itself through a partnership making Glucommander the first software application to run on Roche's cobas pulse hospital glucose meter, and the company markets heavily against a 2026 CMS glycemic quality mandate.

Synapse Medicine builds what it calls a medication intelligence platform, and the distinctive thing about it is where the machine learning sits. Rather than learning to make a prescribing recommendation, the company uses natural language processing to read and classify medication information from three kinds of unstructured source, manufacturer product documentation, official recommendations from health authorities, and published research, and to assemble that into a structured knowledge base that then drives interaction checking, prescription support and medication reconciliation. A separate product applies the same approach to pharmacovigilance itself, automating the coding and prioritisation of adverse drug reaction reports for the centres that receive them. The company was founded in Bordeaux in 2017 by Clement Goehrs and Louis Letinier, both physicians, with Bruno Thiao-Layel, and has raised roughly 39 million dollars across three rounds through a Series B in March 2022 led by Korelya Capital, with backing from a healthcare professionals' insurer and public investment banks rather than from pharmaceutical money. Its institutional position in France is unusual: users include the national medicines agency and the digital health agency alongside Assistance Publique Hopitaux de Paris, the Hospices Civils de Lyon and the Bordeaux teaching hospital, plus more than a hundred hospitals and dozens of telemedicine companies. The company states that it is completely independent of the pharmaceutical industry.

YouScript computes the cumulative effect of a patient's whole medication regimen together with their pharmacogenomic results, rather than checking drug pairs one at a time. Its founder, Kristine Ashcraft, makes the argument directly: American practice has relied on binary interaction alerting for decades, and binary alerting breaks down once a patient is on five or more medications, which now describes tens of millions of people. The patented engine models drug to drug interactions, drug to gene interactions and phenoconversion, where one drug alters how a patient metabolises another and effectively changes their genotype in practice, then returns severity coded guidance across four levels from change to no change alongside safer alternatives within the same drug class or indication. It draws on more than 19,000 high evidence references covering over 2,000 medications and integrates with Epic, Cerner, Allscripts and Telus Health as well as through standalone applications and APIs. The product was incubated for over a decade inside Genelex, a pharmacogenetic testing laboratory in Seattle, established as YouScript in 2016, acquired by Invitae in 2020, and sold to Aranscia in an all cash transaction announced in November 2023. It holds something rare in this category: a prospective randomised controlled trial with a rehospitalisation endpoint. It also markets itself as laboratory agnostic while having been owned by a testing business for essentially its entire existence.

Arine sells medication intelligence to health plans rather than to hospitals, which makes it the clearest payer side record in this category. The platform ingests clinical, socioeconomic and behavioural data across a plan's membership, identifies members at risk of medication related problems before they produce an event, generates personalised care plans for clinical pharmacists to act on, and separately analyses prescriber behaviour so a plan can direct education and recommendations at the clinicians writing the prescriptions. A third product line targets the Medicare Advantage and Part D quality measures, particularly the triple weighted adherence measures that carry disproportionate weight in the CMS Star Ratings that determine a plan's bonus payments. The company was founded in San Francisco by Yoona Kim, a doctor of pharmacy with a doctorate who serves as chief executive, alongside Penjit Moorhead as chief technology officer and David de Vries as chief operating officer, and it launched under the Arine name in 2019 after operating as Akeso. It has raised roughly 95 million dollars across six rounds through a Series C in June 2025, and holds a HITRUST risk based two year certification obtained in July 2024. Named clients span Medicaid, Medicare Advantage and behavioural health, including the Oklahoma Health Care Authority, Magellan Health, VNS Medicare and MDX Hawaii. Every outcome figure the company publishes is its own, though it names the analytic method behind them, which is more than most.

DoseMeRx is a Bayesian precision dosing platform for high risk parenteral medications, computing an individualised regimen from clinically validated pharmacokinetic models, patient characteristics, measured drug concentrations and, where available, genotype. It covers 42 drugs and reaches more than a thousand clinicians across more than a hundred locations spanning the United States, Europe, Asia Pacific, Africa and South America, which is the widest geographic footprint in this category. The product originated in Brisbane, Australia, and its corporate history is unusually eventful for software a hospital pharmacy depends on daily: Tabula Rasa HealthCare acquired it in a deal reported at up to 30 million dollars, completing in January 2019, and sold it four years later to an affiliate of Fairlong Capital on 20 January 2023, with customer contracts transferring to DoseMe Operations Inc. and Paul Edwards installed as chief executive. It now operates from Houston. Three owners in six years is a supplier continuity question a procurement committee should ask about directly rather than a criticism. What sets the record apart is disclosure. This is the only vendor in the category so far to publish where United States patient data physically resides, to state plainly that the customer owns the data it enters, and to describe in public the licence it takes back over de identified data for model improvement. It also holds HITRUST certification and claims to be the only Bayesian dosing platform that does.

PrecisePK is Bayesian therapeutic drug monitoring software from Healthware, Inc. in San Diego, and it has the longest continuous clinical lineage of any product in this category. It began as T.D.M.S. 2000, developed by Philip Anderson, a doctor of pharmacy, with pharmacokinetic consultants, and programmed by Anjum Gupta, a computer scientist, both professors at the University of California, San Diego, and beta tested by the Applied Pharmacokinetics Service at UCSD Medical Center and Rady Children's Hospital. It became a desktop product and later a web platform, and the company marks more than thirty years of partnership with UC San Diego Health across nine University of California sites. The software takes a patient's characteristics, laboratory results and dosing history, assigns them to a population pharmacokinetic model, and updates that model against measured serum concentrations to compute a regimen against an area under the curve, peak or trough target, with the Bayesian method able to work from a single randomly drawn level rather than requiring steady state trough sampling. It carries a genuine distinction: an independent group comparing five Bayesian dose optimising programs in critically ill patients found PrecisePK the least biased of the five. The company markets that result heavily, and a buyer should know the study included nineteen patients and also found this product among the harder ones to use. `founded` is left blank because the product lineage predates the current company branding and no founding year is published.

FeelBetter applies machine learning to polypharmacy, the problem of an older patient accumulating enough medications that the regimen itself becomes the hazard. The platform synthesises longitudinal pharmacy and clinical data across a whole population, identifies the patients at highest risk of near term deterioration or hospitalisation attributable to their medication regimen, recommends specific regimen changes to a clinical pharmacist, and monitors what happens after the intervention. It is organised as four tools: Insight for risk stratification, Navigator for workflow, Action for the pharmacist facing recommendation engine, and Impact for post intervention monitoring. The company was founded in 2018 by Liat Primor, previously VP of global portfolio and chief of staff at Teva Pharmaceuticals, and Yoram Hordan, and operates from Boston and Tel Aviv. It raised 5.9 million dollars in July 2023 bringing total funding to roughly 8 million, which is modest relative to the health systems it serves. Named customers span three distinct buyers: Atlantic Health System in New Jersey across its accountable care organisations, Americare in long term care, and Leumit Health Services, an Israeli health maintenance organisation. The evidence position is unusual for a company this size, since investigators at Brigham and Women's Hospital published a retrospective cohort of 108,817 patients in the American Journal of Managed Care evaluating the platform's recommendations. The company markets its approach under a coined term, Pharmaco-Clinical Intelligence, which describes a market position rather than a method, and almost no technical, security or compliance detail is published alongside it.

InsightRX builds model informed precision dosing software, which computes an individualised dose for a drug whose therapeutic window is narrow enough that getting it wrong causes harm. The core product, Nova, combines published population pharmacokinetic models with maximum a posteriori Bayesian forecasting: it starts from what is known about how a drug behaves in a population, updates that against the concentrations actually measured in this patient, and returns a regimen. A companion product, Apollo, aggregates the resulting data so an institution can examine dosing effectiveness across its own population. The company was founded in 2015 in San Francisco and its platforms are used by more than a hundred institutions and life science organisations across the United States and Europe, covering more than a hundred drugs and therapeutic areas including infectious disease, oncology and transplant. Vancomycin dominates the published record. What distinguishes InsightRX in this category is inspectability. The pharmacokinetic models it runs are named published models rather than proprietary ones, the company publishes which model it selects as default for which patients and the analysis behind that choice, and it has published its machine learning approach to model selection in a peer reviewed pharmacometrics journal. It is also one of the vendors whose software reaches clinicians through a competitor's product, since the precision vancomycin dosing tool inside Premier's TheraDoc clinical surveillance platform is InsightRX. The regulatory position deserves care from any buyer: the software carries a European CE mark obtained by self certification as a Class I device under the superseded Medical Devices Directive, and it holds no United States clearance of any kind.

NarxCare is a set of scores and visualisations layered on top of state Prescription Drug Monitoring Program data, built by Bamboo Health, formerly Appriss Health, which also supplies the underlying platform for more than 40 state and territory monitoring programmes. It is almost certainly the most widely deployed algorithmic risk score in American medicine, reaching hundreds of healthcare facilities and, by the company's account, five of the six largest pharmacy retailers. The report presents three Narx Scores covering narcotics, sedatives and stimulants, a set of configurable state indicators, and an Overdose Risk Score, a three digit value the company states is produced by a logistic regression model evaluating ten inputs drawn from dispensation history such as prescriber counts, pharmacy counts and morphine milligram equivalents over varying windows. Bamboo Health is explicit that the score does not predict whether a patient will experience an overdose, that it is a correlative summary against the histories of people who died of unintentional overdose, and that it must never be the sole justification for providing or refusing medication. The record here is unusual in the index because the company's security and compliance engineering is among the strongest of any vendor graded, while its algorithmic accountability is among the weakest, and the gap between those two is a choice about what to publish rather than a failure of capability. Peer reviewed informatics literature records that the score has been validated only on a subset of three states, has never been evaluated clinically, and has no published subgroup analysis, and in 2025 clinicians petitioned the Food and Drug Administration to determine whether it is a regulated device at all.

MedAware is an Israeli company whose platform monitors prescribing for medication related risk using machine learning rather than the rule libraries that conventional medication decision support runs on. It was founded in 2012 after a nine year old asthma patient died because his physician selected the wrong drug from an electronic prescribing dropdown, an error no interaction rule was written to catch because the prescription was internally consistent and simply belonged to the wrong patient. The engine applies outlier detection over longitudinal and real time patient data, an approach the company compares to fraud detection in financial services, and flags prescriptions that are anomalous for this particular patient rather than prescriptions that violate a predefined rule. Products span prescribing error detection, evolving adverse drug event monitoring, and an algorithm that identifies patients at risk of opioid use disorder. The platform runs continuously rather than only at the moment of ordering, and delivers into existing systems through vendor agnostic APIs, an athenahealth Marketplace listing and, at Ballad Health from 2024, natively inside an Epic pharmacy workflow. MedAware is unusual in this index for the independence of its evidence base, which was produced largely by investigators at Brigham and Women's Hospital and Harvard Medical School and published in JAMIA and the Joint Commission Journal on Quality and Patient Safety, and equally unusual for how little it publishes about its own compliance, security and governance posture.

Clinical decision platform built inside Providence, a 51 hospital health system, and in use there since mid 2022. Founder and chief executive Dr Eve Cunningham, group vice president and chief of virtual care and digital health at Providence. The job it does is narrow and well chosen: supporting the transition between primary and specialty care, telling a clinician whether a referral is needed and, if it is, what workup should happen before the patient is seen. Guidance covers more than 730 conditions and is delivered inside the electronic health record, with the guidance mapped against that patient's own data so the clinician sees knowledge and patient context on one screen. Patient data surfaced from the record is stated not to be stored in the product, which is the second instance in this index of processing patient data without retaining it. The record is filed under decision support rather than clinical reference because it takes patient context and returns a patient specific next action, which is the test this index applies. It is cross listed to reference because the underlying asset is a curated guidance library. One scoping caution. The published account of how the platform was built, in a peer reviewed journal, states that all content and algorithms were built by clinicians in a proprietary environment requiring no code. Separate company and trade material describes generative artificial intelligence being integrated into the platform and calls it an artificial intelligence enhanced clinical intelligence engine. Nothing published reconciles those two descriptions or states what the generative layer does, and the capability notes grade accordingly. Corporate position is in transition and should be confirmed before contracting. Providence stated in late 2024 that it was preparing to spin the platform out, the peer reviewed paper carries an author disclosure about participation in commercialisation during 2024 and 2025, and the product website now carries a 2026 copyright notice in the name of a different entity.

Clinical assistant from Doximity, free to verified United States clinicians and reached inside the professional network the company has operated since 2010. Naming is currently in flux across the company's own material, appearing as Doximity GPT, DoxGPT and, in the support documentation, Doximity Ask. A separate record covers Doximity Scribe, the ambient documentation product; this record covers the assistant, following the product scoping rule that splits one company across records where the products serve different jobs. The evidence side has grown into the substance of the product. It answers clinical questions with referenced responses, gives direct access to peer reviewed literature, and lets a clinician select preferred journals and sources which are then prioritised in later answers, which is a degree of user control over the corpus that nothing else in this category offers. A peer reviewed drug reference covering more than 3,200 monographs with dosing, adverse effects and interactions is integrated alongside it. The company draws an explicit line inside its own product between where it retrieves and where it generates. Drug answers are stated not to be generated on the fly but returned from a structured peer reviewed dataset, and the material contrasts that with what it calls guesswork. Output is shaped for clinical reading, with conclusions placed first and comparisons presented as tables. The rest of the surface is documentation and correspondence: chart notes, discharge summaries, prior authorisation and appeal letters, patient education materials, multilingual translation, and coding assistance, with attachments for uploading labs or notes and saved projects for recurring templates. Secure messaging and fax sit alongside for sending material. Published commitments state encryption in transit and at rest, sessions private to the individual clinician, and prompt data never shared and never used to train the models.

All in one clinical artificial intelligence platform for clinics and hospitals, based at a San Francisco office address and operating since at least 2021, when trade coverage described the company. The product line is unusually wide for a company of this size: a clinical chat assistant, an artificial intelligence scribe, an artificial intelligence receptionist handling patient calls and scheduling, a real time interpreter across several languages, a decision support module offering diagnostic insights and treatment suggestions, a medical search engine, and a study companion for examination preparation. Free consumer style resources sit alongside them including a symptom checker, drug and calculator search, a question bank and contests. The company earlier operated a digital musculoskeletal and physical therapy application using computer vision motion tracking, and the current platform represents a change of direction rather than an extension of it. This record is scoped to the clinical evidence and search side, which is why it sits in this category, and is cross listed only to clinical decision support. The scribe and receptionist lines are described here rather than cross listed, because no evidence was located for any of them and propagating unverified product claims across further category pages would not be sound. The published claims require care and the capability notes set out why in each case. A homepage chart reports 99.4 percent on a named medical question answering benchmark against three named competitor models, with no methodology, date or source. A logo panel headed with a statement about being backed by world class technology leaders displays four major technology company marks, and a separate panel displays the marks of eight prominent academic medical centres above a count of professionals using the product. The nature of every one of those relationships is unstated, and none was verified.

Drug and toxicology reference from Merative, the company formed in June 2022 when Francisco Partners acquired the Watson Health assets from IBM. Headquartered in Ann Arbor, Michigan; Merative chief executive Gerry McCarthy, with Sonika Mathur as general manager for Micromedex. Content is curated by clinical experts from primary literature with in line referencing and daily updates, and is used in more than 80 countries by providers, poison control centres, government departments, universities, pharmaceutical organisations and health plans. The artificial intelligence layer is search. Released 23 September 2025, it lets a clinician ask natural language questions about drug information including interactions and intravenous administration, and returns answers drawn from Micromedex content with a citation on every result that opens the underlying source in one click. Interface affordances include suggested searches and follow up prompts. The company describes the capability as clinically validated. Scoping note, because the boundary matters for this index. DynaMedex is a separate joint product combining this drug content with EBSCO's DynaMed disease content, and it is graded on the DynaMed record rather than here; a December 2025 agreement additionally routes Micromedex dosing and medication safety content into that product's generative layer. This record covers Micromedex itself. Two facts sit outside what a reference product normally carries and both are on the record. Micromedex is recognised as a drug compendium under United States federal law, which makes its content a determinant of coverage for certain off label uses rather than merely a guide to them. And in a separate arrangement the company has licensed a subset of its drug content into a consumer artificial intelligence answer engine as a premium source. Named number one for point of care drug reference in the 2026 Best in KLAS report, for the second time.

Generative chat interface over the American Society of Clinical Oncology's own library of clinical practice guidelines, announced May 2025 and built with Google Cloud. Chief executive Clifford Hudis. Based in Alexandria, Virginia. Record scoped to the Guidelines Assistant rather than to the society, whose meetings, journals, research programmes and certification activities are outside what this index grades. The corpus is drawn solely from ASCO's published guidelines, which rest on systematic reviews and the work of expert oncologist panels, and is updated as guidelines are published or revised. Responses carry citations and sources, and a clinician can ask follow up questions. Access requires an ASCO account and is available to members through the society's site and its member application on both mobile platforms. Two things make this record structurally different from the rest of the category. The first is that the model layer is named openly: the tool is built on Google Cloud's Vertex platform using Gemini models, and no other vendor graded in this category names its model family or its provider. The second is the position the society occupies. ASCO authors the guidelines that define standard of care in medical oncology, operates a certification programme under which other vendors' oncology pathway products are assessed, and now also ships a product built on its own guidelines. The index already records a separate oncology pathways product carrying an ASCO designation. The published disclaimer is unusually precise and is carried on the record because it bounds the product properly: the assistant is described as a voluntary educational resource that does not account for variation among patients and does not substitute for the independent judgment of a licensed healthcare professional. User feedback is stated to refine both the tool and the development of the guidelines over time.

Clinical search platform built for the National Health Service, from Medwise AI Ltd, registered in England and Wales and now based in Leeds having started in Cambridge. Co founded in 2019 by Dr Keith Tsui, a doctor, and Luis Ulloa, who came from applied machine learning. The distinguishing feature is what it indexes. Alongside national guidance from the National Institute for Health and Care Excellence and other national sources, it ingests an individual trust's own local policies, formularies and antimicrobial guidelines, and answers a natural language question across both in one place. No other vendor in this category holds national and local institutional guidance in the same answer surface: the reference incumbents index published evidence only, and the institutional knowledge tools index only what the hospital wrote. The platform states conformance to the clinical risk management standards that govern health information systems in England, covering both the manufacturer's safety case and the deploying organisation's, and its published terms carry an explicit statement that the platform involves artificial intelligence and automated decision making when returning results and generating responses, framed by the company as something good governance requires it to say plainly. Evidence includes a peer reviewed prospective pilot conducted with a Welsh health board on emergency and acute medicine queries, and an evaluation funded through a national innovation agency which estimated substantial time savings per search. Set against that, a headline claim of a 25 percent consultation time saving was published without a supporting reference, and an independent reviewer testing the platform in general practice reported finding a filtered link list rather than synthesis and no saving of that scale. Both are on the record. Commercial model is unclear from public material: earlier accounts describe free sign up open to any healthcare professional, later third party accounts describe enterprise licensing not available to individual clinicians.

Drug information service delivered as software, from an Atlanta company operating since at least 2014. Co founders Ashish Advani, a pharmacist and the chief executive, and Tulasee Rao Chintha, chief technology officer. The company's own one line description is the clearest statement of what it is: clinical pharmacist reviewed, artificial intelligence generated drug information, so that a health system's own pharmacists spend more time with patients. The architecture is the reason this record matters to the category. A clinician submits a free text clinical question and states how urgently they need it. An engine parses the inquiry; a drug information pharmacist formulates a researchable question from it; the engine generates sub questions using prompt templates and large language models, retrieves against a vector database, and produces a candidate summary; and a board certified drug information pharmacist then reviews that summary, discards it if it does not fit the inquiry, and composes the response that reaches the clinician. Prior pharmacist judgements feed back as reinforcement signal into later retrieval. The assistant is named Sherlock and the vector infrastructure is named openly. Output is a custom literature search with citations rather than a database lookup, spanning guidelines, randomised trials, case reports and tertiary analyses. A second arm serves pharmacy and therapeutics committees with drug monographs, class reviews and formulary management, and that is where the company's stated commercial case sits, since formulary standardisation carries measurable savings. More than 30 health systems are stated to retain what the company calls a virtual drug information pharmacist, and an earlier figure gives more than 10,000 physicians, nurse practitioners and pharmacists using it. The company also runs a one year drug information fellowship that trains the pharmacists who staff the service. Investors include Qlarant Capital and Atlanta Ventures. One published limit belongs on the record because it is unusually direct: the company states plainly that it is not a covered entity or a business associate platform for protected health information and instructs users not to submit any.

Clinical artificial intelligence reference platform from CaryHealth, launched August 2024 on the back of the company's GalenAI acquisition and available at askclair.ai, on web and as native mobile applications. Chief executive Areo Nazari, a pharmacist by training; chief technology officer Matt Hawkins. Record scoped to Clair rather than to CaryHealth, whose other products are a direct to patient platform for manufacturer access programmes, a field medical tool for sales representatives and medical science liaisons, a care gap closure product for health plans, and a nationally licensed pharmacy. The product is organised as six purpose built tools rather than a single answer box: open search, guided clinical summary of a disease or drug, drug interactions, antimicrobial stewardship covering treatment, dose and duration with optional patient demographics, plain language definitions of diagnosis codes, and medication review. Sources named are the Food and Drug Administration drug package inserts, the National Institutes of Health clinical trials database, the National Library of Medicine drug interaction database, clinical guidelines, and any documents the customer uploads. Every response carries links to its references, and the company describes intelligent sourcing that routes a question to the source type it fits. Two things a buyer should read together. The corpus and the six tools are overwhelmingly pharmacologic, so despite the general clinical framing this is closer to a drug and therapeutics reference than to a disease reference of the kind UpToDate or DynaMed sell. And the enterprise offering includes custom document ingestion, white labelling, an application programming interface, and customer defined artificial intelligence guardrails, sold into an industry list that names pharmaceutical manufacturers first. A change worth recording: the August 2024 launch release claimed the semantic search approach produced answers with zero hallucinations. That claim does not appear on the current product material, which uses the more careful language of sourcing exclusively from credible clinical sources with reference links.

Generative clinical reference layer built on ClinicalKey, Elsevier Health's point of care content platform. Launched February 2024 as the publisher's answer to conversational clinical search, and materially expanded in February 2026. Record scoped to ClinicalKey AI rather than to Elsevier, whose portfolio spans scientific publishing and research analytics with no clinical relevance, following the UpToDate and DynaMed precedent. Note also that a separate Elsevier product, ClinicalPath, was assessed for this index in July 2025 and rejected for carrying no artificial intelligence claim; the rejection does not carry over to this record. The corpus is a proprietary, copyright cleared knowledge base refreshed daily, spanning Elsevier point of care content, reference texts including Braunwald's Heart Disease, Goldman-Cecil Medicine and the Nelson Textbook of Pediatrics, society guidelines from bodies including the American College of Cardiology and the European Society for Medical Oncology, and, since February 2026, full text from more than 130 premium journals including The Lancet and the New England Journal of Medicine. Earlier material described third party journals as abstracts only, so the February 2026 release is a real change in what the model can read rather than a restatement. Three mechanisms distinguish it. Responses carry traceability to the specific paragraph in the source rather than to the document. A real time citation validation step checks that a generated response is grounded in the evidence it cites. And the clinician can toggle explicitly between adult and paediatric context, which makes the population a set parameter rather than an assumption. Access is through web, a mobile application with voice dictation, single sign on into the record using the Smart on Fhir standard, and an application programming interface. Continuing education and maintenance of certification credit accrue from use. Available in more than 50 countries. One structural fact belongs on the record: the product was developed in partnership with OpenEvidence, which is separately indexed here and now a direct competitor at scale.

Point of care disease reference from EBSCO Clinical Decisions, a division of EBSCO Information Services, and one of the two reference incumbents this segment is measured against alongside UpToDate. Content rests on a stated seven step evidence based methodology with daily systematic literature surveillance, edited by a named clinical team under editor in chief Dr Peter Oettgen. Record scoped to DynaMed and its generative layer rather than to EBSCO, whose wider portfolio spans library and research databases with no clinical relevance, following the UpToDate and ModMed precedent. The AI is Dyna AI, introduced July 2024 as a retrieval augmented generation capability sold as an add on, extended in February 2026 into Dyna AI Mode, a dedicated interface inside DynaMed, DynaMedex and Dynamic Health that a clinician can toggle in and out of rather than being forced through. The company states the generative layer draws only on its own curated content sets and never on external unvetted sources, labels AI generated output, and links each answer to its origin. Dyna AI Mode also captures questions, responses, references and timestamps in copy ready form and allows a prior query to be re run, which is closer to an audit trail than anything else graded in this category. DynaMedex is a separate joint surface combining DynaMed disease content with Micromedex drug content from Merative, and was named top performing for clinical decision support point of care disease reference in the KLAS 2026 report; DynaMed itself was Best in KLAS in 2021, 2022, 2024 and 2025. A December 2025 announcement extends Dyna AI to generate structured drug monograph and dosing summaries from Micromedex data. EBSCO Clinical Decisions joined the Coalition for Health AI in September 2024 and publishes five principles for the responsible use of AI covering quality, security and patient privacy, transparency, governance and equity. Two scope statements are carried on the record because the company publishes them plainly and they bound what a buyer actually gets: Dyna AI is currently unavailable in the European Union, and DynaMed with Dyna AI for individual subscribers is currently available only in the United States.

Clinical evidence search engine for licensed clinicians, operated by Veracity-Health Inc. and free to verified clinicians and trainees with unlimited searches. The product is built on a retrieval first architecture that the company states is deliberately the reverse of generation first assistants: it searches a corpus given as more than 60 million peer reviewed papers, guidelines and care pathways, ranks what is relevant, applies evidence grading logic the company likens to the work of a guideline methodologist, then generates a short answer with inline citations tied to specific statements. Alongside search it ships drug and interaction checking, a large calculator library, differential and treatment planning views, and specialty tailored literature updates, and it accrues continuing education credit per search, which is the second instance of that model in this category after UpToDate. In March 2026 the American College of Emergency Physicians announced a partnership placing ACEP clinical policies inside the product, confirmed on ACEP's own newsroom and quoted by ACEP president L. Anthony Cirillo. Direction comes from a named international clinical council of sixteen physicians chaired by Scott M. Silvers, formerly chair of emergency medicine at Mayo Clinic and chief medical officer for knowledge management at Optum Health. Founded by Maxime Allouch and Taieb Bennani, both from MIT, and backed by Y Combinator and Gradient Ventures. Two scope notes carried on the record: every published performance figure is vendor run and no peer reviewed evaluation of the product was located, and the company publishes comparison content about other vendors on its own blog, so its characterisations of competitors are a self interested source.

Clinical pathways authoring and governance platform that converts a health system's guidelines, policies and protocols into structured visual decision trees delivered inside the EHR workflow. The product covers clinical pathways and what the company calls ClinApps, guidelines and protocols, policies and regulatory content, antimicrobial stewardship and antibiograms, and embedded clinical calculators, with all content searchable, versioned, governed and measurable. Authoring is visual and requires no code, and pathway logic stays under clinician control without information services involvement after the initial integration, which is the operational claim the company leads on. A community layer lets organisations share and copy each other's pathways across institutional boundaries and across different EHRs. Governance is presented as the core feature rather than a compliance wrapper: named clinical owners, defined review cycles, multidisciplinary review workflows, version history and audit trails, and visible status showing what is live, outdated or under review. The AI scope is deliberately narrow and the company states it plainly on a dedicated page: artificial intelligence is applied to structuring and drafting content from guidelines and PDFs, to supporting updates as evidence changes, and to scaling pathway volume without proportional staffing, under a stated principle of human in the loop at every stage, transparent logic and provenance, and no autonomous clinical decision making. The marketed position is explicitly against opacity, arguing that healthcare needs clarity, transparency and accountability rather than black boxes, and that decision logic should be explicit rather than implicit. Reported at more than one million patient lives impacted, with OSF HealthCare among named implementations and a concentration in paediatric and children's hospital settings. Founded by emergency medicine physicians Eric Leroux and Dan Imler.

Clinical deterioration early warning and clinical pathways company built on research from the University of Chicago, indexed primarily on eCART, its FDA cleared early warning system. eCARTv5 is a cloud based gradient boosted machine learning model, integrated into the EHR, that synthesises routine vital signs, laboratory data and patient demographics into a single score predicting the composite outcome of death or ICU transfer for adult ward patients. The 510(k) summary discloses the model class outright, publishes the default alerting thresholds of 93 for moderate risk and 97 for high risk on a 0 to 100 scale constructed from specificity, and states that the observed rate of deterioration at each threshold is displayed to the clinician as odds of deterioration in the next 24 hours alongside the score, which puts calibration in front of the user at the point of care. The cleared indication restricts the model to data a healthcare professional has already validated, so unconfirmed streaming data from monitors and devices is excluded until a nurse or clinician confirms it. Clearance rested on retrospective validation across 1,769,461 encounters and prospective validation across a further 205,946 encounters in three geographically distinct health systems, and the filing publishes performance stratified by five race categories and by comorbidity, which no other vendor in this category publishes anywhere. The company separately sells Clinical Pathways, a content and workflow product with more than 4,800 pathways live across adult, paediatric and neonatal topics in ambulatory, emergency, inpatient, ICU and women's health settings, which serves as the response layer the cleared eCART workflow directs clinicians into. Co founded by Dr Dana Edelson, Chief Medical Officer, whose University of Chicago group produced the underlying research across more than fifty peer reviewed publications since 2011. CEO and co founder Borna Safabakhsh.

Cytovale's IntelliSep is an FDA cleared in vitro diagnostic that assesses a patient's probability of sepsis by measuring the mechanical behaviour of their own white blood cells rather than by looking for a pathogen or a biochemical marker. It is indexed here rather than in inpatient monitoring because it is a point in time test performed on a blood sample, not continuous surveillance. The underlying observation is that when leukocytes become activated, as they do in sepsis, their biomechanics change: size, deformability, stiffness and behaviour under fluid stress all shift. The Cytovale System exploits this by taking 100 microlitres of whole blood, lysing the red cells and washing the leukocytes, then passing tens of thousands of individual cells through a microfluidic junction where hydrodynamic forces deform them. Ultra high resolution imaging captures each cell during that deformation at very high frame rates, and high throughput image analysis, computer vision and machine learning translate the resulting cell images into a single score. Results return in roughly eight to ten minutes. The output is the IntelliSep Index, a value from 0.1 to 10.0 stratified into three published bands: Band 1 from 0.1 to 4.9 indicating low probability of sepsis, Band 2 from 5.0 to 6.2 indicating intermediate probability, and Band 3 from 6.3 to 10.0 indicating high probability. The cleared indication is precise: adult patients with signs and symptoms of infection presenting to the emergency department, to aid early detection of sepsis with organ dysfunction manifesting within the first three days after testing, with results intended for use alongside clinical assessment and other laboratory findings. Clearance came under K220991 in December 2022, supported by the multicentre CV-SQuISH-ED clinical validation study whose national principal investigator was Hollis O'Neal MD of Our Lady of the Lake Regional Medical Center and LSU Health Sciences Center. The underlying science traces to deformability cytometry work by Dino Di Carlo at UCLA, who along with the Regents of the University of California holds a disclosed financial interest in the company. Cytovale is based in San Francisco, led by co founder and chief executive Ajay Shah, and raised a 100 million dollar Series D led by Sands Capital in October 2024 with participation from CPP Investments, Norwest Venture Partners, Global Health Investment Corporation and Breakout Ventures. Pricing is not published; the company has applied for a CPT Proprietary Laboratory Analyses code.

HealthLeap runs continuous AI screening across every hospitalised patient, starting with malnutrition. The problem it targets is a screening failure rather than a detection failure: hospitals typically assess nutritional risk through a questionnaire completed by nursing staff on admission, which is static, so a patient who is adequately nourished on day one and declining by day four is missed. Nationally, the company cites 30 to 50 percent of hospitalised patients as being at risk of inadequate nutrition while fewer than 9 percent are formally diagnosed. The platform runs in the background of the electronic health record from admission through discharge, analysing the full chart daily rather than at a single point: clinical notes, laboratory results, vital signs, active medication orders and problem lists. Risk scores and prioritisation alerts are pushed into the native EHR interfaces that nurses and dietitians already use rather than into a separate application. The company describes malnutrition as the first condition rather than the only one. HealthLeap publishes a dedicated clinical use and safety statement that is more explicit than most in this category. It states that the software is not a diagnostic tool, does not provide treatment recommendations, must not be used as the sole basis for determining nutritional risk or the presence of malnutrition, and should be used alongside clinical assessment, physical assessment and laboratory findings. It also states a clear technical boundary: the software uses only existing EHR data and evidence based risk factors, and does not acquire or process medical images, waveforms or signals from medical devices. The commercial case is made in two halves and the second is worth reading carefully. Alongside earlier identification and shorter length of stay, the company positions the platform as improving documentation accuracy so that the record reflects a patient's true severity of illness, aligning frontline care with hospital coding, optimising reimbursement accuracy and defending against retrospective payer claim denials. HealthLeap is based in San Francisco and announced a systemwide deployment across Houston Methodist covering more than 150,000 inpatients annually. It claims to be the only commercially available peer reviewed validated platform of its kind, a claim this record was unable to verify against a retrieved publication. Pricing is not published.

AlertWatch:OB is a maternal safety surveillance system for labor and delivery, and its distinguishing feature is that alarm fatigue was the founding design problem rather than an afterthought. The company was founded in 2012 as a University of Michigan spinout by Dr Kevin Tremper, chair of the university's anesthesiology department, and the founding team included Dr James Bagian, a former NASA astronaut who was the founding director of the VA National Center for Patient Safety and the first chief patient safety officer for the Veterans Health Administration. Tremper has described designing the system around aviation cockpit principles and the lesson of Three Mile Island, where every alarm sounding at once proved distracting rather than useful. The product monitors mothers rather than fetuses, and covers an unusually wide span of the obstetric journey: from triage through labor and delivery, into operating rooms and post anesthesia care units, and through the postpartum period. It integrates hundreds of data elements from physiological monitors, the EMR, laboratory systems and medical history, and alerts clinicians using a proprietary maternal early warning score based on national obstetric standards, with proprietary filtering intended to raise alerts without producing alarm fatigue. It assesses ACOG postpartum hemorrhage risk every minute for every mother, and, notably, checks automatically whether blood is available and whether intravenous access is adequate, so the system tracks the hospital's readiness to respond rather than patient risk alone. AlertWatch:OB is FDA 510(k) cleared and the company describes it as the first and only cleared software system dedicated solely to maternal safety in labor and delivery. It follows AlertWatch:OR, cleared in 2014 for the operating room, and AlertWatch:AC for acute care. Commercial launch of the OB product was announced in January 2020, at which point it had assisted with more than 10,000 births, rising to more than 15,000 in reporting later that year. Distribution has run partly through Clinical Computer Systems, maker of the OBIX electronic fetal monitoring system, as both reseller and co marketing partner. The company retains a relationship with the University of Michigan covering intellectual property and ongoing product testing. AlertWatch was acquired by BioIntelliSense in 2022. Pricing is not published.

PeriGen sells the PeriWatch platform, an early warning and clinical decision support system for labor and delivery. It is the obstetric member of this category, and the only vendor assessed here whose algorithms were validated by experts at a federal research institute rather than by the company or its customers. PeriWatch Vigilance continuously monitors both the mother and the fetus, drawing from the hospital's existing electronic fetal monitoring system and EMR, and notifies clinicians when maternal vital signs, fetal heart rate patterns or labor progress assessments cross hospital defined safety limits. It escalates to designated hospital leaders when thresholds are breached, and is accessible through an obstetric command center view and on mobile. The architecture is a hybrid worth noting: the fetal heart rate pattern interpretation and uterine contraction detection use supervised machine learning, which the company states plainly, while the alerting thresholds themselves are set by the institution rather than the vendor. The underlying algorithms have a long regulatory and research history. Patterns performs automated fetal heart rate pattern recognition and Curve assesses labor progression, accounting for factors including contraction frequency and epidural use. The company describes these as the only FDA cleared algorithms for fetal heart rate interpretation and labor progress assessment commercially available in the United States. Patterns 3.0 was cleared in February 2025, extending the indicated range from 36 weeks of gestational age down to 32 weeks to cover earlier monitoring of high risk pregnancies. PeriWatch Surveillance is separately 510(k) cleared. Independent validation came from three experts at the Eunice Kennedy Shriver National Institute of Child Health and Human Development, who reviewed the software's analysis across 100 tracings and reported agreement in over 97 percent of assessments, concluding that computerised fetal heart rate interpretation shows substantial agreement with expert evaluation and can screen in real time when an expert is not continuously watching. Separate peer reviewed work in the American Journal of Obstetrics and Gynecology compared the software against five expert clinicians using a strict classification framework and found the computer results not statistically different from the clinicians. PeriGen is based in Cary, North Carolina, is led by chief executive Matthew Sappern, and is a Halma company. It acquired the WatchChild fetal monitoring system from Hill-Rom. Named deployments include Mount Sinai Medical Center, Avera Health and NYC Health + Hospitals. Pricing is not published.

Healthplus.ai sells PERISCOPE, a CE certified clinical decision support system that predicts the risk of postoperative bacterial infection. It carries the most methodologically complete published evaluation of any vendor in this category, and its central research contribution is the problem this category keeps running into: that a model developed at one hospital does not necessarily work at the next one. PERISCOPE reuses data already in the electronic health record, requiring no new measurements, and draws on roughly 50 clinical parameters covering the preoperative and intraoperative period. It produces two predictions per patient, one for infection within seven days of surgery and one within 30 days, presented to surgical teams as three traffic light categories inside the EHR workflow. The scope covers postoperative bacterial infections broadly rather than surgical site infection alone, including pneumonia, urinary tract infection and other bacterial infections. The models are XGBoost, and the company states they are calibrated to each hospital's own data. The validation work was published in The Lancet Regional Health Europe in December 2024. Models were developed at one hospital and then validated and updated at two further hospitals in the Netherlands and Belgium, across 253,010 surgical procedures using data from 2014 to 2023 spanning multiple surgical specialties, with the two most recent years held out for temporal validation. Performance was reported on discrimination, on calibration including slope, intercept and plots, and on clinical utility through decision curve analysis with net benefit. The study was funded by a REACT EU grant from the European Regional Development Fund rather than by the company, and conflicts are disclosed: the chief executive is a major shareholder and two authors are employees, while the substantial academic co author list declares none. The product holds ISO 13485 and CE certification as Class IIa software as a medical device under the EU Medical Device Regulation, a route that requires clinical evaluation. The company has stated it is licensed to operate in the EU with initial focus on Benelux and Germany while pursuing FDA authorisation for the US. Named clinical sites include Amsterdam UMC and Deventer Ziekenhuis. A 2.3 million euro round was led by Elevating Capital and LUMO Labs with Pathena Venture Capital, Leistone and ROM InWest. Pricing is not published.

Luminare sells inpatient sepsis screening and intervention software, and it is the only vendor in this category whose founding argument is that better prediction is not the problem. The company's position, stated on its own site and argued in a peer reviewed review article co authored by its chief executive, is that the health technology industry has concentrated on speed of detection while outcomes stall because clinical staff frequently fail to act on the alert. Its answer is not a stronger model but an automated intervention workflow. The mechanism is correspondingly transparent. Luminare screens patients on admission through EMR integration and then once per shift, combining information already in the medical record with the nurse's own assessment, structured around what the company calls an enhanced SIRS screening checklist. When criteria are met it drives the hospital's existing sepsis protocol, issuing clear intervention steps in SBAR format, routing inter departmental communication, and reporting performance against the hospital's own benchmarks. It also supports SEP-1 bundle documentation for the three and six hour bundles. Notably, the nurse's assessment is an input to the screen rather than a review step applied afterwards, so unlike a background risk score the output cannot be passively ignored. The company was founded in 2014 in Houston by Sarma Velamuri MD, a board certified internal medicine physician and hospitalist, and Marcus Rydberg, following the death of a friend's daughter from septic shock. It is based at the Texas Medical Center Innovation Factory. Luminare signed an enterprise agreement with Cedars-Sinai in 2023 after participating in that health system's accelerator, and names Microsoft, Cerner, CPSI and the American Heart Association Get With The Guidelines programme as interoperability partners. The company reports 36 staff. Pricing is published as a three tier structure with the unit of pricing named: a standalone product requiring no integration offered on a 30 day trial, an Enterprise Lite tier on a flat annual fee deployable in weeks, and an enterprise platform priced per patient day on an annual contract and implemented over months. A separately published grant programme offers three, six or twelve month deployments at no financial cost to qualifying smaller facilities, in exchange for data sharing, focus group participation and testimonials. Luminare is not FDA cleared.

AITRICS is a Korean medical AI company whose AITRICS-VC (VitalCare) predicts patient deterioration from electronic medical record data. It is the first vendor in this category focused on the general ward rather than the ICU, which is a materially different detection problem: ward patients are observed intermittently rather than monitored continuously, so the model must work from sparse, irregularly timed measurements. As marketed, VitalCare runs two deep learning models built on a bidirectional long short term memory architecture. VC-MAES predicts clinical deterioration events, defined as unplanned ICU transfer, cardiac arrest or in hospital death, within six hours. VC-SEPS predicts sepsis onset within four hours. The company also markets cardiac arrest prediction within 24 hours and, in the ICU, mortality prediction within six hours. Inputs are 19 parameters drawn from the EMR: six vital signs, 11 blood test results, level of consciousness and age. The development and validation work is published as a medRxiv preprint. Models were derived on 357,009 adult general ward admissions at Yonsei Severance Hospital between 2013 and 2017, then externally validated on 22,073 admissions at National Health Insurance Service Ilsan Hospital. In external validation VC-MAES reached an AUROC of 0.918 against 0.834 for MEWS and 0.883 for NEWS, and VC-SEPS reached 0.941 against 0.559 for SOFA, 0.687 for qSOFA and 0.767 for NEWS. Both models held AUROC above 0.86 across all age and sex categories, which is the only published subgroup performance reporting located for any vendor in this category. Regulatory footprint spans several jurisdictions, and the scopes differ in ways a buyer must read carefully. Korea's MFDS approved the deterioration prediction product for both general wards and intensive care. Hong Kong's Medical Device Division and Vietnam's Ministry of Health followed. The US clearance is materially narrower: 510(k) K240756, granted 23 July 2024 under 21 CFR 870.2300, describes software performing rule based calculation of conventional early warning scores including NEWS, MEWS and qSOFA, screening patients against predefined thresholds and displaying them on a dashboard. It is indicated solely for the general ward and is expressly not indicated for the ICU or operating rooms, and the submission required no clinical data. VitalCare is reported in use at more than 60 hospitals in Korea. Pricing is not published.

Etiometry sells a clinical intelligence platform for critical care that combines vendor neutral ICU data aggregation with four FDA cleared physiologic risk indices. It is the most technically disclosed vendor assessed in this category, and it takes a fundamentally different approach from the machine learning classifiers that dominate the lane. The indices do not predict a coded outcome. They estimate the probability that a patient is currently in a defined physiologic state, and each state is specified against a published clinical threshold. IDO2 estimates the likelihood of inadequate oxygen delivery against a configurable mixed venous oxygen saturation threshold. IVCO2 estimates the likelihood of hypercapnic respiratory failure. HLA estimates the likelihood of hyperlactatemia, defined as lactate above 4 mmol per litre. ACD estimates acidemia, defined as arterial pH below 7.25. Each runs continuously using what the company describes as a Bayesian modelling approach combining mechanistic models of human physiology with techniques drawn from aerospace navigation, taking heart rate, blood pressure, SpO2 and, where available, filling pressures, venous oxygen saturation, hemoglobin and blood gases. The models are built to tolerate missing or intermittent inputs, which the company identifies as a common failure point for simpler rule based early warning scores. Around the indices sits a data layer with unusual reach. Etiometry ingests numeric data at five second intervals and waveforms up to 500 Hz from bedside monitors across GE Healthcare, Philips, Draeger, Mindray, Nihon Kohden and Spacelabs, ventilators from Medtronic, Draeger, Hamilton Medical and Getinge, ECMO, VAD and CRRT systems, cerebral oximetry and hemodynamic monitors, using medical device integration platforms including Capsule, Bedcomm and IBUS. EHR data arrives from Epic, Oracle Health and Meditech over HL7 and a FHIR application with SMART on FHIR single sign on, and derived risk indices can be pushed back out over HL7 and REST. A Quality Improvement Application retains ingested data with full fidelity waveforms archived separately, and supports pathway adherence reporting and event reconstruction. The company reports 11 FDA clearances covering the platform, persistent bedside display functionality, the four risk indices and, in 2026, the Cardiogenic Shock Tool under K254066, alongside EU MDR, Health Canada and MDSAP authorisations, ISO 13485, ISO 27001 and SOC 2 Type II. Deployment is stated at roughly 150 ICUs and 3,500 beds across 50 hospital partners. The published evidence base is concentrated in paediatric cardiac critical care and includes a multicentre before and after study in Critical Care Medicine and work in Circulation: Cardiovascular Quality and Outcomes. Pricing is not published.

CLEW Medical sells FDA cleared machine learning models that predict clinical deterioration in hospital critical care, delivered through a virtual ICU platform. It is the only vendor assessed in this category whose absolute operating characteristics are published in a public document, because the models are regulated as medical devices and the 510(k) summaries state them. The cleared device, the CLEWICU System, contains two models. CLEWHI estimates the likelihood that a patient will develop hemodynamic instability requiring vasopressor or inotrope support within the next eight hours. CLEWLR indicates that a patient is at low risk of deterioration, which supports step down and discharge decisions. The system ingests EHR and patient monitoring data over an HL7 connection and runs on infrastructure the hospital provides. Around the models sits a tele ICU platform with unit level situational awareness, a rounding worklist, a configurable rules based notification tool called NotifyMe, and deep links back into the medical record. Regulatory history is the deepest in this category. An Emergency Use Authorization for the respiratory deterioration model was granted in June 2020. The first 510(k), K200717, cleared in 2021 and was described by the company as the first FDA clearance for a device predicting hemodynamic instability in the ICU. A second clearance, K233216, was granted on 13 January 2024 under 21 CFR 870.2210, product code QNL, Class II. That submission did two things: it broadened the intended use environment from the ICU alone to all hospital critical care areas including emergency department resuscitation, post anesthesia care, step down and specialised units, and it demonstrated that the models met the same pre specified performance criteria after being retrained on a reduced feature set of 50 inputs rather than the original 80. FDA also cleared a Predetermined Change Control Plan permitting further retraining without a new submission, subject to stated conditions. Evidence spans the regulatory file and the peer reviewed literature. The 510(k) validation was a retrospective cohort study run independently across two datasets, the University of Massachusetts eICU dataset of 6,534 patient stays and the public MIMIC III dataset of 5,069 patient stays. A 2023 paper in CHEST compared the models against telemedicine system alerts and bedside monitor alarms at UMass Memorial and WakeMed. Named deployments include UMass Memorial, WakeMed Health and Hospitals, EQUUM Medical, Sheba Medical Center and Tel Aviv Sourasky Medical Center. The company maintains US operations out of Boston. Pricing is not published.

Birth Model applies machine learning to labor and delivery, and the product has two halves that a buyer should assess separately. The operational half predicts delivery timing to synchronise unit operations, forecast staffing and reduce wait times, presented through a cloud platform and an observability view the company calls Motherboard, updating automatically from EHR data. The clinical half performs risk stratification for postpartum haemorrhage, preeclampsia and shoulder dystocia, and the company claims lowered primary caesarean rates among its outcomes. Founded and led by Anish J. Shah MD FACOG, a board certified obstetrician gynaecologist at Penn Medicine Princeton Medical Center who reports more than 4,000 deliveries, with Max Guillet as chief technology officer. The clinical systems lead is Epic certified with EpicCare and Stork credentials, Stork being Epic's obstetrics module, and the product is listed in the Epic Showroom with iOS and Android applications alongside the web platform. Backers and partners include Johns Hopkins, Techstars through its Baltimore AI Health programme, CareFirst BlueCross BlueShield and Gurtin Ventures. The advisory board is named with credentials and includes Neel Shah MD FACOG MPP, chief medical officer of Maven Clinic, and Girish Navani, chief executive of eClinicalWorks, as investor and strategic advisor. The method is covered by granted United States patents 11,664,100 and 12,133,741, with a further application pending, disclosed through a virtual patent marking notice. Filed under clinical decision support because risk prediction at the point of care is what the index has a category for. The operational forecasting half is arguably the larger one and has no natural home in the current taxonomy.

VieCure sells Halo Intelligence, a combined clinical decision support system and oncology electronic medical record built specifically for community cancer care rather than academic centres. The platform generates and monitors personalised treatment plans from diagnosis through therapy and survivorship, drawing on patient data, physician notes, imaging, labs, next generation sequencing, molecular pathology and treatment response, and spanning medical, radiation and surgical oncology. The mechanism matters and the company describes it plainly. Recommendations come from a clinical inference engine reasoning over a codified content library the company reports at more than 5,000 clinical rules, covering treatment plans, pathways, protocols, drug and toxicity management, payer rules, trial eligibility criteria, diagnostics and formularies. In 2025 VieCure signed a non exclusive licensing agreement with the National Comprehensive Cancer Network, adding NCCN template based treatment plans to the platform, so a substantial part of the knowledge base is traceable to the authoritative published guideline body in oncology. Founded in Denver and launched in 2016. A 45 million dollar financing led by Northpond Ventures closed in October 2024, alongside the appointment of Richard Daly as board chairman. The company reports more than 18,000 patients managed daily across a community cancer centre network exceeding 100 locations, with named users including American Oncology Network, The Oncology Institute, Oncology Care Partners and Summit Cancer Centers. Not a surgical decision support product. It covers surgical oncology as one discipline within a cancer care platform whose buyer is a community oncology practice.

Eko Health puts cardiac detection AI inside the stethoscope. SENSORA is its enterprise platform: FDA cleared software that analyses simultaneous ECG and heart sound data captured by an Eko digital stethoscope during a routine exam, and flags low ejection fraction at or below 40 percent, atrial fibrillation, structural heart murmurs and normal sinus rhythm, while calculating intervals including heart rate, QRS duration and electromechanical activation time. Capture can be performed by medical assistants and nurses as part of intake, not only by physicians. The company is led by co founder and chief executive Connor Landgraf and holds a stack of clearances built up over a decade: its digital stethoscope in 2015, then algorithms for structural murmur and atrial fibrillation, the Duo combined ECG and stethoscope, and in 2024 the Low Ejection Fraction tool developed in collaboration with Mayo Clinic. In November 2024 the AMA granted SENSORA a Category III CPT code, 0962T. The evidence base is unusual for this index. A randomised controlled trial across more than 200 NHS primary care practices was published in The Lancet and is described as the largest AI cardiology RCT conducted. An independent validation in Circulation found the murmur algorithm doubled sensitivity for structural heart disease against an analogue stethoscope. A Lancet Digital Health study of over 1,000 patients addressed the low ejection fraction algorithm in primary care. The product is deployed across more than 100 UK clinics through the NHS and Imperial College London. Filed alongside AccurKardia, HeartSciences and Ceribell as a cleared device plus algorithm that detects disease from a physiological signal, rather than as surgical decision support.

RhythmX AI, based in Palo Alto and launched in 2023, sells a precision care platform aimed at primary care physicians. It unites data across more than ten sources, EHR content alongside clinical policies and guidelines, financial, payer, social and lifestyle data, and produces patient specific recommendations covering laboratory tests, imaging, medications, follow ups, social care and referral routing, which a clinician can drill into through a generative interface. It is EHR agnostic and runs inside Epic at its named customer. The chief executive is Deepthi Bathina, previously chief clinical product officer at Humana, and the company is owned by SAI Group, the private investment firm behind SymphonyAI and ConcertAI. It is filed under clinical decision support rather than clinical summarisation, and cross listed into summarisation, because of what it produces. Chart consolidation is real and clinicians describe it as replacing extensive manual review, but the product's distinguishing output is a forward looking recommendation about what to do next rather than a report of what the record already contains. That is the boundary this index draws between the two categories. The substantive evidence is a single named deployment and it is a good one. Presbyterian Healthcare Services in New Mexico, a nine hospital system with its own statewide health plan, expanded the platform to 200 primary care clinicians in February 2026 in what the companies describe as the first full system deployment of a precision care AI platform. Within weeks of the initial pilot, clinicians identified and reviewed more than 200 combined HCC and non HCC conditions across more than 10,000 patient encounters, and the system's chief medical information officer is on record describing consolidated information at the point of care and an assistant answering patient specific questions. A clinical advisory board carries named executives from Prime Healthcare, Sentara Health and Mass General Brigham. One attribution caution. RhythmX has merged with the patient engagement company Get Well and now also appears as GW RhythmX. The frequently quoted reach of 150 health systems and 85 million patients is the combined figure for both companies and should not be read as this platform's deployment base.

Wellsheet sells a Care Team Copilot that sits on top of the EHR rather than replacing it, reading the full chart to produce prioritised clinical views, narrative summaries, and generated documentation for hospital course, assessment and plan and discharge summaries. An embedded chat agent answers patient specific questions inside the EHR, and an AI Pathways feature walks evidence based pathways, presenting its chosen answer alongside the supporting evidence drawn from the patient's own chart. The company is explicit about the distinction from ambient scribes: it does not listen to the encounter, it reads what is already documented. It is inpatient and care team centred rather than physician only, covering multidisciplinary rounding across physicians, nursing and case management, and discharge planning with a dashboard tracking geometric mean and average length of stay plus alerts for patients due for discharge. Contextual views adapt to the user's specialty, role and usage patterns. Clinical content is licensed rather than generated, with UpToDate pathways, calculators and lab interpretations pre filled with patient specific data. Two pieces of corporate context matter to a buyer. Elsevier has acquired Wellsheet, positioned as closing the gap between patient data and clinical evidence at the point of care. That is a continuity positive relative to a venture funded independent, and it raises one specific question worth asking directly: the integration the product is built around is UpToDate, which belongs to Wolters Kluwer, a direct competitor of Elsevier in clinical reference content. Confirm in writing that the UpToDate integration survives, and on what terms. Second, Ascension has deployed Wellsheet system wide, confirmed by Ascension's own newsroom, alongside named use at Baptist, Indiana University Health, Robert Wood Johnson, Concord and San Juan Regional Medical Center. One caution about reading the marketing. The company's own website carries a footer disclaimer stating that certain features described are illustrative of potential capabilities and are not currently included in the product. It does not say which. Disclosing this at all is more candid than most of the market, and it also means no feature on the site can be assumed to ship. Confirm each capability against a live instance before it enters an evaluation.

Abstractive Health retrieves a patient's full longitudinal record from the national health information exchange networks and summarises it, with every sentence in the summary linked back to the source note it came from. Founded May 2022 in New York City by Vince Hartman, Ritika Poddar and Giordana Pulpo, out of Cornell Tech and a research partnership with Weill Cornell Medicine. Hartman was previously a product manager at Epic and at Robin Healthcare. Two cofounders were named to Forbes 30 Under 30 for Healthcare. The company is a Certified B Corporation. The product is a direct implementer on Carequality and CommonWell rather than a reader of one EHR, and it performs geospatial retrieval around a patient's current and prior addresses to assemble a record from institutions the treating organisation does not share a system with. Incoming data arrives as CCDAs, FHIR R4 resources, PDFs and image based documents including handwritten scans, and is normalised into a single structure, with the original CCD and FHIR files available for download. Outputs include medical summaries, handoff notes, discharge summaries, transition of care notes and machine readable JSON, CDA and FHIR. It runs as a SMART on FHIR app, a web app or a Chrome extension, and names twelve supported EHRs. A retrieval augmented question answering module answers chart questions with citations, and a separate module issues clinical decision support alerts. The reason this record matters to the category is the evidence. In December 2024 the company's own team published a peer reviewed cohort study in JAMA Network Open, with Weill Cornell and NewYork Presbyterian co authors and institutional rather than vendor funding, evaluating its summaries against physician written notes across 1,600 emergency medicine records. On conventional automated summarisation metrics the model beat the physicians. On a purpose built clinical evaluation framework it was marginally inferior on usefulness and safety, with safety concerns arising in 6 percent of cases, the named failure modes being data omissions and mischaracterisations. The authors concluded that a physician in loop design is necessary. Almost nothing else in healthcare AI publishes a result that undercuts its own product, and buyers should weigh it accordingly. They should also note that the company markets the same study under the badge Clinically proven, which is a considerably warmer characterisation than the study's own conclusion supports.

Regard reviews the entire electronic health record and recommends diagnoses to the clinician at the point of care, then generates the note around them. Founded 2017, based in Los Angeles and New York City. The company frames this as a diagnosis problem rather than a documentation problem: its stated premise is that physicians see roughly 3 percent of the data in a chart, and its diagnostic intelligence layer reviews the rest and surfaces conditions with the supporting chart evidence attached. Four modules run off the same engine: Clinical Notes, Mid Revenue Cycle, HCC Capture and Screening. In July 2025 the company added Proactive Documentation, combining chart data with ambient conversation from the room, plus an agent named Max, and described the platform as expanding from a hospitalist tool to system wide coverage. Named health system customers include Sentara Health, WakeMed, Penn Highlands Healthcare, Kettering Health, Main Line Health, FirstHealth, Westchester Medical Center, UAMS, Eisenhower Health and Torrance Memorial. Sentara reports a 17 percent increase in CC and MCC capture alongside a 4x return per user. The company reports 12,993,284 recommended diagnoses accepted by clinicians, and publishes site level figures including 50 million dollars in revenue earned at an Arizona health system, 9.3 million dollars in denials prevented at a North Carolina system and a 20 percent reduction in queries at a Pennsylvania system. Two things a buyer should weigh. First, Regard holds a regulatory artifact that is rare in this category and independently verifiable: an ONC Health IT Module certification, number 15.04.04.3192.Rega.01.00.0.240502, certified 2 May 2024 against 2015 Cures Update criteria covering electronic health information export, authentication and related privacy and security criteria. That is neither FDA clearance nor a HIPAA attestation, and no device authorisation or published clinical decision support exemption analysis was located for a product whose core function is recommending diagnoses. Second, every headline outcome the company publishes is financial or operational. None of them measures whether the recommended diagnoses were correct, and the acceptance figure is published as a count of accepted recommendations without the number recommended, so an acceptance rate cannot be derived from it.

Avo is a clinician built decision support platform whose foundation is content rather than a model: Avo Pathway is a library of digitised medical society guidelines and calculators, which health systems customise or supplement with their own protocols, and which knowledge publishers can embed their intellectual property into. The AI layer sits on top of that. Avo Assistant covers pre charting, care planning, ordering and ambient documentation, and Chart Assist produces a patient snapshot with care gaps, diagnosis and treatment recommendations. Its ambient scribe is therefore not a standalone note writer: it drafts orders, identifies care gaps, suggests dosing, and enhances notes with differential diagnosis, clinical documentation improvement and coding. Two capabilities stand out for this index. It captures conversations with three or more participants across 50 or more languages, naming child, parent, translator and clinician explicitly, which is the hard case most scribes avoid. And its recommendations are anchored to named published guidelines including society pathways, MCG milestones and RAND consensus pathways rather than to model inference alone.

Clinical AI built for health plans rather than providers, which is what distinguishes it in this category. Where most indexed RCM vendors help providers get paid, Anterior automates the clinical review work inside payer organisations: prior authorization, risk adjustment, care management and payment integrity. Founded 2022 by Abdel Mahmoud, a physician, and Zahid Mahmood, with the premise drawn from observing that thousands of nurses and clinicians inside payer organisations are pulled away from patient care to perform administrative tasks that nonetheless require clinical judgement. The technical claim is a proprietary clinical reasoning architecture rather than general-purpose generative AI applied to medical text, which matters because the task is evaluating whether a specific patient's documented clinical picture meets a specific payer's medical necessity criteria, not summarising a chart. The company reports serving payer organisations covering over 50 million lives, 99.24 percent clinical accuracy, and a 76 percent increase in auto-approvals for customers. Funding totals roughly $63 to $64 million across three rounds, including a $20 million Series A led by NEA in 2024 and a $40 million round in February 2026, with investors including Sequoia Capital, NEA, FPV and Kinnevik. Context that makes this category consequential right now: sweeping federal prior authorization and interoperability requirements took effect from January 2026, mandating electronic submission and faster turnaround, which is driving payer investment in exactly this automation. That timing cuts both ways, and the index should say so. The same underlying capability that speeds legitimate approvals is the capability behind automated denials, and industry commentary in 2026 documents payers deploying machine learning to issue rapid denials on high-cost services. Anterior's reported metric is increased auto-approvals specifically, which is the favourable direction, but buyers and readers should understand that a clinical reasoning engine inside a payer is a utilization control system regardless of which direction it is tuned.

Calgary based cardiovascular imaging software company whose cvi42 platform is long established across cardiac MRI and CT reading, with cvi42 Plaque receiving FDA 510(k) clearance in October 2025 for AI-enabled coronary plaque analysis quantifying total, calcified and non-calcified plaque. The defining characteristic is deployment architecture, and the company leads with it rather than with accuracy claims: cvi42 Plaque runs on-premise, so coronary artery disease evaluation happens without sending patient data to an external reading service. The CEO frames this directly as giving clinicians greater control over their data, improved study processing times and workflow efficiency, and a Johns Hopkins user describes gaining immediate and interactive control over the analysis. That makes Circle the clearest architectural counterpoint in the cardiovascular CT lane: Heartflow and Elucid both route scans to vendor-side processing, Artrya is cloud-based point-of-care with no external reading teams, and Circle is fully on-premise inside the institution. Buyers with data residency constraints, sovereignty requirements, or objections to imaging leaving the enterprise have effectively one option in this category. The product integrates into existing CT workflows and is stated to be compatible with all major vendor systems, reflecting its heritage as a multi-vendor reading platform rather than a single-algorithm point solution. The company also tracks the reimbursement position closely and describes it accurately, noting Medicare coverage of AI-enabled coronary plaque analysis and the transition from Category III code 0625T with national payment set at $950 to a permanent Category I code effective January 2026, alongside higher base payments for the underlying CCTA exam.

Australian medical technology company, ASX listed as AYA, commercialising the Salix AI cloud platform for near real-time point-of-care assessment of coronary artery disease from CCTA. The platform is modular and each module carries its own regulatory status: Salix Coronary Anatomy was cleared in March 2025, Salix Coronary Plaque received FDA 510(k) clearance in August 2025 under K251837, and Salix Coronary Flow was completed with clinical data validation underway ahead of a further 510(k) submission. The plaque module sits inside the same user interface as the anatomy platform and was enabled in the live product immediately on clearance. The design emphasis is speed and self-sufficiency: assessments in under 10 minutes without switching systems, and explicitly no external reading teams validating scan data, so physicians retain direct control rather than waiting on a vendor-side processing service. That is a genuine architectural difference from Heartflow's analysis-service model. Plaque volume is computed by identifying voxels within target Hounsfield Unit ranges located between the lumen and outer vessel wall, and the product reports plaque features including low attenuation plaque, spotty calcification, positive remodeling and the napkin-ring sign, presented through a personalised 3D heart model. The algorithm was built from thousands of scans contributed by institutions in the US, Australia and Canada. Commercial deployment is live at Tanner Health across five hospitals generating fee-per-scan revenue, with Northeast Georgia Health and Cone Health integrating. The SAPPHIRE study is a retrospective multi-centre real-world study across six major US health systems including Piedmont Healthcare, running in three phases to evaluate the prognostic utility of a proprietary Plaque Dispersion Score, with a stated focus on coronary artery disease in women given that a large majority of women who die of coronary disease had no prior symptoms.

Digital cognitive screening built for population scale deployment inside health system workflows rather than for depth of assessment, which is the strategic difference from others in this category. The core product is a three minute patient-guided test producing instant objective scoring with DSM-5 aligned output, deliberately sized to fit inside a Medicare Annual Wellness Visit without adding clinician burden or requiring specialised training to administer. The company positions cognitive function as something to be checked routinely like a vital sign, targeting the detection gap it cites where roughly 80 percent of adults aged 65 to 80 receive no routine cognitive screening. Founded 2012 by Elli Kaplan with academic co-founders including Elizabeth Buffalo and cognitive impairment researcher Stuart Zola, with leadership backgrounds at UCSD, the Atlanta VA Medical Center and Emory. Reports 25 peer reviewed papers, 11 patents, FDA Class II registration, more than $62.8 million in venture funding plus $4.3 million in grants from the National Institute on Aging, the Alzheimer's Drug Discovery Foundation and the Georgia Research Alliance, and over 200,000 assessments delivered. Named health system customers include Sutter Health, Providence and Stanford Medicine, with reported reach across more than 15,000 clinicians. An important historical correction the company makes itself and which older coverage gets wrong: Neurotrack originated in eye-tracking based cognitive assessment, and much third party writing still describes it that way, but the company states plainly that while digital eye tracking was among its first technologies it is not part of its current clinician product suite, which now uses patient-led tests selected for validity, accessibility and ease of administration. Tests use culturally agnostic symbols and numbers by design. Also supports RADV audit documentation and Medicare Advantage quality workflows.

Digital musculoskeletal clinic pairing computer vision motion tracking with a clinical care team of physicians, physical therapists, nurses and health coaches. NYSE listed as HNGE. The technology core is genuinely substantial rather than a wrapper: the company acquired wrnch in 2021, the developer of a leading computer vision platform for measuring human motion using the same class of three-dimensional tracking employed in elite sport and motion capture for film, and stated at the time that the acquisition gave it the largest computer vision team in digital health. That became TrueMotion, which turns an ordinary smartphone camera into a 3D motion assessment tool, detecting 87 reference points across the body with no specialised equipment, and provides live audio and visual feedback on exercise form. In October 2025 the company launched Movement Analysis, which uses TrueMotion to capture joint angles, symmetry and endurance and combines those objective measures with questionnaires to produce HingeScore, plus Robin, an AI care assistant for members. It also published a set of AI care principles governing development across the platform. The strategic argument is worth understanding: almost all established MSK outcome measures rely on patient self-report, so an objective movement measurement captured at home is a genuine measurement contribution rather than a workflow convenience. The company also sells Enso, an FDA cleared wearable delivering electrical nerve stimulation for pain relief. Serves self-insured employers and health plans at large scale. Indexed with a clear scoping note: this is a care delivery organisation with a strong technology layer, not a software vendor, and the AI is graded on that basis. Sits alongside Sword Health, its closest competitor, also indexed.

The regulatory originator of computerised cognitive testing in the United States. Cognivue holds a De Novo classification, DEN130033, validated against the St. Louis University Mental Status examination, which makes it the first FDA cleared computerized cognitive test and means it created the device category that later entrants such as BrainCheck and Linus Health were cleared into. The same relationship Viz.ai has to computer aided triage. The technology rests on adaptive psychophysics rather than digitised paper instruments: the test dynamically adapts as the patient progresses, calibrating to each individual's visual and motor abilities, and collects over 130,000 data points to produce a single clinical score. Two self-administered products share the cleared technology, Clarity, a 10 minute assessment covering memory, executive function and attention, discrimination and visuospatial domains plus two performance parameters, and Thrive, an abbreviated 5 minute screening covering three domains and designed for a busy office or pharmacy setting. Both are self-administered by the patient without a clinician proctor, which is the central operational difference from competitors and the basis of the company's claim to remove administrator bias and variability. Devices are calibrated identically across units to support consistency and retest reliability. The evidence base is substantial and independent: a published validation reported Cognivue as equally effective as the Montreal Cognitive Assessment with superior test-retest reliability, and the FOCUS study enrolled 452 participants across six US sites, comparing against the Repeatable Battery for the Assessment of Neuropsychological Status specifically to establish performance across age, education, sex, race and ethnicity strata. Products are indicated as adjunctive tools for evaluating cognitive function, explicitly not stand-alone diagnostics.

Digital cognitive assessment built on machine learning analysis of drawing and speech behaviour rather than on digitised test scoring, which is the distinction that separates it from most of this category. The flagship DCTclock analyses the entire process of drawing a clock, capturing temporal and spatial features of stylus manipulation across the whole drawing rather than scoring the finished picture, and extracts cognitive and motor metrics no paper version can produce. Digital Clock and Recall (DCR) adds immediate and delayed word recall to assess verbal memory, runs in about three minutes, and the LinusAD algorithm combines age, APOE status, drawing metrics, speech and acoustic features and stylus dynamics. The suite now exceeds a dozen assessments including a digital Trail Making Test Part B, a Digital Assessment of Cognition, gait and speech measures, and the electronic Person Specific Outcome Measure. Technology originated in over a decade of R&D at Lahey Hospital & Medical Center and MIT. The commercially important capability is biomarker prediction, which puts this product in a different business from cognitive screening: peer reviewed work found DCTclock significantly correlated with amyloid PET in cognitively normal individuals and outperforming the 30 minute Preclinical Alzheimer's Clinical Composite at distinguishing amyloid positive from amyloid negative, a Harvard Aging Brain Study longitudinal cohort of over 200 cognitively normal adults found higher amyloid or tau burden associated with faster DCTclock decline, and in the 930 participant Bio-Hermes-001 study the DCR predicted amyloid PET status. The company also reports the DCR predicting plasma p-tau217 status. That matters because disease modifying Alzheimer's therapies require confirmed amyloid pathology, so a three minute office test that predicts PET status addresses the screening failure and prescreening cost problem directly. Deployed to health systems with EHR integration and a remote administration option. Note the company's own positioning acknowledges the complementary rise of FDA cleared blood based biomarkers rather than competing with them.

The company that created the non-invasive coronary physiology category and now the broadest platform in the cardiovascular CT lane. Nasdaq listed as HTFL. The Heartflow One platform spans four functions across the CCTA pathway: RoadMap Analysis for rapid stenosis detection, FFRCT Analysis for the physiological significance of each lesion computed from a previously acquired CCTA, Plaque Analysis for quantification and staging, and Planner for intervention planning. FFRCT is the origin product and its distinguishing status is guideline recognition rather than clearance alone: it is supported by the ACC/AHA Chest Pain Guideline, which is a materially higher bar than a 510(k) and something no competitor in this lane holds. Plaque Analysis and RoadMap Analysis were cleared in October 2022, with two further clearances reported in May 2026. Critical regulatory scoping that buyers outside the US must catch: all four functions are cleared for clinical use in the United States, Bahrain, Israel, Saudi Arabia and the UAE, but only FFRCT and Planner are cleared in Europe, the United Kingdom, Australia, Canada and Japan. Plaque Analysis and RoadMap are therefore not available for clinical use in most of the developed world outside the US. The evidence base is the largest in the index for any single vendor: more than 600 peer reviewed publications, over 100 studies assessing more than 365,000 patients, and clinical use across more than 400,000 patients worldwide. Landmark studies include SCOT-HEART, the EMERALD series, the ADVANCE-DK registry with seven year follow up, SMART-CT showing a 25 percent reduction in read times, the PRECISE trial, a Mass General Brigham registry of over 15,000 patients with up to 16 years of follow up, and the DECIDE registry of roughly 22,000 patients across more than 30 US centres. Editorial note worth carrying: when the Nature Reviews Cardiology QCI Study Group benchmarked eight commercially available FDA approved plaque quantification systems head to head, the publication records that Heartflow was unable to provide the required analysis for inclusion. The largest evidence base in the category does not necessarily mean the greatest willingness to be compared directly against competitors, which is the axis on which Lunit earned credit in this index.

Oxford spinout measuring coronary inflammation rather than plaque, from the same routine coronary CT angiography scan. This is what separates it from every other vendor in the cardiovascular CT lane. Coronary inflammation inhibits lipid accumulation in adjacent fat cells, producing a measurable gradient in the perivascular adipose tissue surrounding the artery, and the CaRi-Heart FAI-Score quantifies that gradient as a Fat Attenuation Index. The clinical significance is that it detects risk in patients who look clean on conventional assessment: in the Oxford led ORFAN registry, among patients with no or minimal plaque and zero calcium at CCTA, those with the highest coronary inflammation showed roughly 9.5 to 11 times the cardiac mortality risk and around 5 times the major adverse cardiac event risk compared with those with minimal inflammation. The company operates two distinct products with different regulatory standing, and buyers must not conflate them. CaRi-Plaque, for plaque and stenosis quantification, received FDA 510(k) clearance in March 2025 and sits in the same competitive set as Cleerly, Elucid, HeartFlow and Artrya. CaRi-Heart FAI-Score, the inflammation product and the actual differentiator, is regulatory cleared in the UK, EU and Australia but remains pending FDA clearance for US clinical use, holding only a Category III CPT code approved by the AMA panel. A Category III code is an emerging technology tracking code and is not the Category I code with established payment that applies to automated plaque analysis. Evidence is unusually strong and independent: ORFAN was published in The Lancet as a multicentre longitudinal cohort externally validating the AI-Risk prognostic algorithm in an NHS population, the CRISP-CT study established incremental prognostic value of perivascular FAI beyond traditional risk factors, an ESC Congress 2025 late breaker reported FAI-Score predicting cardiac death beyond hsCRP, and a prospective trial NCT07220304 is registered. Cloud based deployment.

Digital cognitive assessment and care planning platform. The flagship product, BrainCheck Assess, is a Class II FDA regulated device that delivers a standardized battery of digital cognitive tasks covering memory, executive function, attention, mental flexibility and processing speed, drawn from digital versions of established neuropsychological instruments including Stroop, Trail Making and Digit Symbol Substitution. It sits within a wider platform of more than 30 clinical evaluation tools, screeners and care planning instruments that clinicians can combine into custom protocols. Co-founded by neuroscientist David Eagleman. More than 400,000 cognitive assessments have been completed on the platform. The evidence base is comparatively strong and is published rather than asserted: a validation study of 99 participants reported at least 88 percent sensitivity and specificity separating normal cognition from dementia and at least 77 percent separating mild cognitive impairment from the other groups; a later study found a Pearson correlation of 0.77 against the Montreal Cognitive Assessment; and a 529 participant multi-site study conducted with BrainBox Solutions and registered as NCT04423198 assessed test retest reliability and concurrent validity against traditional paper and pencil tests. Remote self-administration has been reported as statistically indistinguishable from in-person coordinator led sessions. Important scoping note: the AI content of this product is limited. The battery is a digitized and normed set of conventional neuropsychological tests with an expanding normative database, not a machine learning diagnostic model, and the company states the platform is not intended as a stand-alone diagnostic tool and is designed to support clinician interpretation rather than replace it. Indexed for the assessment platform, and graded accordingly on AI Centrality.

AI analysis of coronary and carotid CT angiography that quantifies and classifies atherosclerotic plaque morphology. PlaqueIQ received FDA 510(k) clearance in October 2024 and is distinguished by its ground truth: the convolutional neural networks were trained on carotid endarterectomy histopathology images co-registered to CTA scans, so plaque components are classified against actual tissue rather than against Hounsfield unit thresholds. Because Hounsfield values overlap between tissue types such as lipid rich necrotic core and intraplaque hemorrhage, the software interprets the spatial distribution of tissue properties across adjacent voxels instead of applying fixed HU ranges. The company markets this as CT Virtual Histology. The product reports patient, vessel, and lesion level plaque composition, with particular emphasis on lipid rich necrotic core, a plaque type associated with heart attack and stroke risk. A carotid artery version launched in October 2025, extending the same approach to stroke risk. An FFRCT product derived from the plaque algorithm, which the company argues yields concordance between plaque findings and ischemia estimates, remains pending FDA clearance and should not be treated as available. Reimbursement is established rather than theoretical: four of the seven Medicare Administrative Contractors extended coverage for AI enabled quantitative coronary plaque analysis from November 2024, the radiology benefit manager EviCore added it to coverage guidelines, and United Healthcare aligned its cardiac imaging guidelines to include coronary CTA plaque quantification across all plans from October 2025. Note that the workflow is not fully autonomous: trained analysts segment the data to create the 3D coronary model before the software classifies and quantifies tissue. Indexed alongside Cleerly, its closest comparator, with which it is routinely benchmarked in the peer reviewed literature.

Taiwan based medical imaging AI company spanning digital pathology infrastructure and diagnostic algorithms, whose aetherSlide image management platform received both FDA clearance and IVDR certification in 2026, positioning it for global expansion beyond Asia. Its algorithm portfolio includes a CE marked lymph node metastasis detector for gastric cancer that classifies and quantifies positive and negative nodes, and aetherAI Hema, described as the first bone marrow differential AI system, trained on a curated dataset of more than one million cells and reporting 15 subtype differential counts. Deployed across Chang Gung Memorial Hospital branches and developed in collaboration with National Taiwan University Hospital and Chi Mei Medical Center. Backed by Quanta Computer and Cathay Venture, and has applied to list on the Taiwan Innovation Board.

Chinese origin imaging AI company with US operations, whose InferRead suite spans lung nodule detection and characterization on CT, chest radiography, coronary CTA, and target reconstruction, alongside an InferOperate surgical planning suite covering thorax, liver, and urology. Holds the broadest multi jurisdiction regulatory footprint of any vendor in this lane, with authorizations from the US FDA, European CE, UK UKCA, Japan PMDA, and China NMPA. Its flagship lung CT product was first FDA cleared in 2020 and received further clearance in May 2025 for enhanced features including detection of nodules as small as 4 mm and automated lung density analysis. Deployed for tuberculosis screening programmes including UN procured deployments in Eastern Europe and Central Asia.

Formerly Owkin Dx, the CE-IVD marked diagnostics arm of Owkin, since spun out as an independent company named Waiv with $33M in financing. This record keeps its original address for continuity. It remains indexed separately from Owkin itself under the index rule of indexing by AI product rather than by company, and Owkin holds a separate record covering its research and pharmaceutical business. Two products established the position, both category firsts in Europe. RlapsRisk BC predicts risk of distant relapse in ER positive HER2 negative early invasive breast cancer from haematoxylin and eosin stained whole slide images combined with clinical variables including age, node involvement and tumour size, described as the first CE-IVD approved digital pathology AI diagnostic predicting relapse risk. MSIntuit CRC pre-screens colorectal tumours from routine histology to rule out microsatellite stable phenotypes, reducing the volume of confirmatory MSI testing required. Both are positioned as cheaper and more accessible alternatives to molecular and gene expression testing, using slides laboratories already produce. Following the spin out the portfolio is presented under an AI native digital pathology platform named Destra. Buyers should note that certifications and compliance disclosures published by Owkin cannot be assumed to transfer to the spun out entity, and should be confirmed with Waiv directly.

Oslo based company whose Histotype Px Colorectal predicts patient outcome in stage II and III colorectal adenocarcinoma from standard H and E stained slides, stratifying patients into low, intermediate, and high risk groups to inform whether adjuvant chemotherapy should follow surgical resection. Reported as the first CE marked product using AI to predict patient outcome from image analysis. Built on research from the Institute for Cancer Genetics and Informatics at Oslo University Hospital and trained on close to 100 million image tiles. Addresses a specific overtreatment problem the company states plainly: most stage II and III patients are cured by surgery alone, so a large majority receiving adjuvant chemotherapy gain no benefit while incurring its harms.

French imaging AI company whose Milvue Suite reads plain radiographs across trauma and chest, detecting fractures, dislocations, joint effusions, pneumothorax, opacities, pleural effusions, and nodules, and returning a pre-filled structured report to the RIS or PACS in under 30 seconds alongside roughly 60 automatic measurements. Holds two FDA clearances, TechCare Trauma for musculoskeletal detection and triage plus a chest clearance, with CE Class IIa marking under EU MDR via BSI and Health Canada Class II licensing. Reports analysis of more than 25 million X-rays in 2024 across 600 or more sites spanning Europe, North America, Latin America, the Middle East, and Africa. Offered as both cloud and on-premises deployment, and also distributed through the Blackford imaging platform.

Romanian imaging AI company whose platform analyses chest X-rays, detecting and localizing a reported 148 findings, alongside lung nodule detection on CT with longitudinal comparison across a patient's prior scans to track change over time. Notable for tuberculosis detection specifically, where its performance was assessed in a South African tuberculosis prevalence survey published in The Lancet Digital Health, an unusually independent and public health oriented validation setting for a commercial imaging vendor. CE marked and integrated directly into on-premise or cloud PACS, returning annotated images and structured outputs inside the native radiology viewer, with an integrated reporting engine that converts detections into draft report text for radiologist validation.

UK company whose red dot platform sits at the autonomous end of the imaging AI spectrum: it classifies chest X-rays as High Confidence Normal and auto-reports them, removing those exams from the radiologist worklist entirely, while triaging suspected lung cancer cases for priority reporting, with a companion CT head product. Peer reviewed work in Clinical Radiology reported auto-reporting of 15 percent of chest X-rays as normal at a 0.33 percent error rate, and a separate study reported a 60 percent reduction in missed lung cancer when triage ran alongside consultant radiologists. Important corporate history: the original Behold.ai Technologies Limited entered administration in January 2025 after failing to win contracts under the NHS AI Diagnostic Fund, and NICE withdrew red dot from its recommendations in February 2025. A successor entity, Behold.ai Global Technologies Limited, subsequently acquired the intellectual property and assets and relaunched the platform under new leadership.

South Korean medical AI company whose flagship DeepCARS predicts in-hospital cardiac arrest within 24 hours for patients in general wards, using only four routinely collected vital signs drawn from the EMR: blood pressure, heart rate, respiratory rate, and temperature. Distinctive in this index for operating on biosignals rather than images, in a market where Korean medical AI was largely image based, and for the deployment scale it has reached in one national system, more than 48,000 hospital beds across South Korea including 20 tertiary general hospitals. Approved by Korea's MFDS in 2021 and holding US FDA Breakthrough Device Designation since 2023 while pursuing 510(k) clearance.

Deep learning medical imaging analytics company, formerly Zebra Medical Vision, acquired in November 2021 for up to 200 million dollars and now operating as the AI subsidiary of Nasdaq listed medical imaging hardware company Nano-X Imaging. Its distinguishing strategy is opportunistic population health screening: mining CT scans already acquired for unrelated clinical reasons to surface undiagnosed conditions, with cleared products covering vertebral compression fractures and low bone density associated with osteoporosis via HealthOST, coronary artery calcium quantification for cardiovascular risk, plus pneumothorax and brain bleed detection. Reported ten FDA clearances across the portfolio. Sold to hospitals, HMOs, integrated delivery networks, pharmaceutical companies, and insurers.

Paris based imaging AI company whose Rayvolve suite reads plain radiographs, with AZtrauma covering fractures, joint effusions, and dislocations and AZchest covering lung nodule detection plus pneumothorax and pleural effusion triage. Holds FDA clearances for adult fracture detection since 2022 and pediatric fracture detection since 2024, plus two further chest clearances, and the company states AZtrauma is the only FDA cleared solution in the United States spanning fractures, joint effusions, and dislocations together. Validation is unusually external for a company of its size, drawing on studies run by University Hospitals in Cleveland, SimonMed Imaging, and a 2026 multi-site analysis of 258,373 X-rays across 100 clinical centers in 26 countries.

San Jose company whose Hypercube platform abstracts and reasons over unstructured patient records for chart review, cohort building, and clinical trial prescreening. Its stated technical differentiator is a hybrid approach pairing large language models with symbolic reasoning over a clinical hypergraph, adopted specifically because the company argues pure language model approaches are inadequate for clinical work given hallucination risk. Every answer is traced back to discrete highlighted evidence in the patient's original record, and the platform is cloud agnostic and can be hosted in the customer's own environment so data never leaves. Subject of prospective evaluation at the University of Pennsylvania comparing AI alone, human alone, and human plus AI trial prescreening workflows.

Stockholm based company whose Stratipath Breast is described in peer reviewed literature as the first CE-IVD marked AI image analysis tool for primary breast cancer risk stratification available for routine clinical use. Rather than detecting or quantifying a biomarker, it stratifies patients into low and high risk groups from routine hematoxylin and eosin stained slides already produced in standard care, positioning it as a prognostic alternative to molecular multigene assays that carry longer lead times and higher cost. Validated retrospectively across 2,719 primary breast cancer patients from two Swedish hospitals, and since 2025 also distributed through PathAI's AISight platform.

Palo Alto precision oncology company whose Vesta platform predicts treatment response and prognosticates outcomes from routine hematoxylin and eosin stained slides, built on a Computational Histology AI foundation trained on more than 500,000 pathologist annotations of tumors and their microenvironment. Reaches the clinic through a different regulatory route than most pathology AI indexed here: tests are offered as laboratory developed tests from the company's own CLIA certified and CAP accredited laboratory rather than as cleared devices, spanning bladder, prostate, and pancreatic cancer. Vesta Bladder Risk Stratify Dx received FDA Breakthrough Device Designation in May 2026, reported as the first AI powered digital pathology prognostic test in bladder cancer to do so.

Danish precision pathology software company whose diagnostic products are packaged as APPs, self contained algorithms for specific biomarker scoring tasks, covering HER2, Ki67, PD-L1, lymph node metastasis detection, and invasive tumor detection across breast, lung, prostate, and colorectal cancer. Distinguished by regulatory volume under Europe's IVDR, with the PD-L1 application described as its ninth IVDR clearance, and by a commercial alliance with Agilent under which the APPs are validated for Agilent assays and the Omnis platform and sold as the Visiopharm Diagnostic Package. The APP model reflects a structural reality of pathology AI: clearances are narrow, tied to a specific tumor type, biomarker, antibody clone, and assay vendor, so breadth is achieved by accumulating many separately certified algorithms rather than one general model.

Ohio based thoracic imaging company whose ClearRead suite applies deep learning vessel suppression to chest CT and radiography for lung nodule detection, cleared by FDA in 2016 as the first device supporting concurrent reading, where the AI output is viewed alongside the initial read rather than after it. The portfolio has since extended to coronary artery calcium scoring on ungated non contrast scans, enabling opportunistic cardiac screening from scans acquired for other reasons. An earlier generation of computer aided detection than most vendors indexed here, with a correspondingly narrow scope, but with a documented multi reader clearance study and a long deployment record.

Radiology AI company operating as a joint venture between an Australian healthcare technology firm and a large radiology provider, offering comprehensive detection across chest X-ray and non contrast head CT. Its enterprise products detect up to 124 findings on chest radiographs and up to 130 findings on head CT, alongside a separately cleared triage and notification portfolio covering ten time critical findings in the United States. Notable for holding Medicare New Technology Add on Payment status and for processing a substantial share of chest X-rays in England.

Gleamer SAS is a Paris based radiology AI company, acquired by RadNet, Inc. on 2 March 2026 in an all cash transaction valued at up to 230 million euros, comprising approximately 215 million at closing plus a 15 million contingent milestone, and integrated into RadNet's digital health subsidiary [[deephealth]]. Ownership, the contracting entity and the hosting arrangement therefore now sit under a United States imaging services operator, which is a change a buyer should confirm before signing rather than discover afterwards. BoneView, its principal product, detects fractures, effusions, dislocations, and bone lesions on radiographs, cleared by FDA for both adult and pediatric use and reported as the only AI fracture detection software holding both. The portfolio has expanded into chest X-ray, pediatric bone age, and musculoskeletal measurement, positioned as semi automated pre diagnosis producing structured reports. Distributed directly and through multiple imaging platform partners including other vendors indexed here. At acquisition the company reported more than 130 staff and more than 700 customer contracts across 44 countries, with annual recurring revenue compounding at more than 90 percent a year from 2022 through 2025 and expected to reach roughly 30 million dollars in 2026. It retains a separate record here rather than being folded into the parent because it continues to carry its own brand, product line, clearances and customer base. The acquirer has stated it intends to deploy these tools across its own network of more than 400 imaging centres, where radiography accounts for close to a quarter of volume.

Cardiovascular imaging company applying AI to coronary CT angiography to quantify and characterize atherosclerotic plaque, producing comprehensive phenotyping of coronary artery disease from non invasive imaging. Distinguished in this index by reimbursement achievement rather than clearance alone: its technology underpins Category I CPT codes, multiple Medicare Administrative Contractor coverage determinations, and commercial payer policies reported to span more than 86 million covered lives. Evidence base includes the multicenter international CONFIRM2 registry.

Biomedical AI company building generative and agentic tools for pathology, whose PathChat co-pilot combines pathology foundation models pretrained on histology image and image text datasets with a custom trained multimodal large language model, enabling conversational analysis of high resolution slides and clinical data. The copilot is presented as usable from a microscope, a slide viewer or a smartphone, which removes the usual prerequisite of a completed digital pathology programme. A second product, Judith, is an agent for automating AI model development for biomedical image analysis. Extends research published in Nature from an academic lab at a major Boston hospital system; the company's whole slide foundation model TITAN is described in the literature as pretrained on 335,645 whole slide images and fine tuned partly on synthetic captions generated by PathChat itself. Received FDA Breakthrough Device Designation for its diagnostic version, which the company states plainly does not imply clearance or approval. In January 2026 the company announced its acquisition by AstraZeneca to advance AI driven oncology research and development at global scale; this record grades the products rather than the parent, and the ownership relationship is treated on the governance axis. Earlier research collaborations were announced with Techcyte, also indexed here, and with illumiSonics.

Cancer diagnostics company describing itself as the first and most widely deployed AI platform in pathology, with prostate, breast, and gastric solutions used in routine clinical practice worldwide. Its prostate product received FDA 510(k) clearance as an in vitro diagnostic that generates heatmaps flagging small and rare cancers missed on initial assessment, functioning as a second read safety net rather than a primary reader. Notable in this index for holding an unusually complete set of published security and quality certifications.

Risk adjustment platform built on what the company calls neuro symbolic AI, combining machine learning with an explicit clinical knowledge graph to link every suggested hierarchical condition category code to specific MEAT based clinical evidence. Positions this explainability as audit defense under tightening CMS RADV enforcement, covering prospective, retrospective, and audit workflows for health plans, health systems, and at risk provider organizations.

Cancer focused imaging AI company, publicly listed in Korea, whose INSIGHT suite covers chest X-ray, mammography, and digital breast tomosynthesis. Distinguished less by product breadth than by evidence depth: more than 100 peer reviewed publications spanning The Lancet Digital Health, JAMA Oncology, and Radiology, including third party head to head validations against competing commercial products. Lunit completed its acquisition of Volpara Health Technologies in May 2024. Volpara, founded in 2009 and based in Wellington, New Zealand, made breast screening software covering breast density assessment, imaging quality management and personalised screening, and brought an installed base reported at more than 2,000 United States medical sites. The Volpara name was retired in November 2025, when the business was folded into the Lunit brand and renamed Lunit International, taking commercial operations across the United States, Oceania, Europe and Asia while the Seoul headquarters retained research and new product development. A buyer searching for Volpara or Volpara Health is searching for this vendor. Reported reach after the integration is more than 10,000 healthcare providers across more than 65 countries, with distribution partly through imaging hardware OEM relationships.

AI care coordination platform that pairs disease detection on imaging with automated mobilization of the treating team, originating in stroke where its LVO product received the first ever FDA De Novo authorization for computer aided triage and notification software. The platform now spans more than 50 FDA cleared algorithms across CT, ECG, and echocardiography covering stroke, intracranial hemorrhage, pulmonary embolism, and aortic disease, deployed at over 1,700 hospitals. Its distinguishing mechanism is the mobile app that alerts and connects specialists rather than only flagging a scan.

Lithuanian radiology AI developer whose ChestLink is the first and only regulatory approved application to perform diagnostic reporting autonomously, holding CE Class IIb certification to identify high confidence normal chest X-rays and issue finalized reports with no radiologist involvement. The broader CXR Suite adds detection across 75 findings and a quality module that re checks AI negative studies, with recent CE certified extensions into chest CT and musculoskeletal X-ray. Acquired by imaging IT company Sectra, completed April 2026.

Precision research platform applying AI and natural language processing to structured and unstructured EMR data, including physician notes, pathology, genomics, and lab reports, to match patients and sites to trial protocols in real time. Connects health systems, treating physicians, sponsors, and CROs in a shared ecosystem covering cohort building, site feasibility, patient recruitment, and real world evidence generation. Reports sites finding more than 25 percent additional patients relative to traditional recruitment. Acquired in March 2025 by Tempus AI, a company listed on the Nasdaq, for undisclosed consideration; at the time of the transaction the platform was reported to connect more than 750 provider sites, and the acquirer described the attraction as the integration infrastructure. This record covers the Deep 6 platform. Contracting, assurance and governance questions now run to the parent, and a buyer should establish which entity signs and whether the platform has been migrated onto the parent's infrastructure.

Global radiology AI company with the broadest chest X-ray regulatory footprint in the market, spanning nine products across X-ray and CT. Its qXR suite detects, localizes, and categorizes findings on plain film chest radiographs including lung nodules, pneumothorax, and pleural effusion, alongside qER neurocritical products covering intracranial hemorrhage, cranial fracture, and midline shift. Notably the only chest X-ray computer aided detection device cleared by the FDA with a Predetermined Change Control Plan, allowing model updates without a new submission.

Clinician copilot that ingests data across the EHR, health information exchanges, insurance claims, and care gap files, then uses proprietary language models to classify documents and extract structure from free text, producing a consolidated Patient Portrait at the point of care. Surfaces suspected chronic conditions never explicitly documented, infers hierarchical condition categories for risk adjustment, and supports care gap closure, with every insight linked back to the underlying clinical evidence. Sold primarily into value based primary care.

Medically specialized AI company whose Synapsis AI platform automates electronic medical record chart review and abstraction, answering specific clinical questions from structured and unstructured records rather than generating summaries. Applied to clinical trial pre screening, observational studies, registry reporting, and quality measurement. Deployed enterprise wide at a major academic health system, with results published in a peer reviewed cardiology journal.

Clinical trial platform that deploys AI matching inside provider EHRs to identify trial eligible patients within routine care, combining structured data prefilters with large language models that reason over full text clinical notes and nuanced eligibility criteria. Pairs the technology with recruitment coordinators working alongside site teams, plus site feasibility automation and a trial design service for sponsors. Operates a research ready provider network spanning community oncology practices, health systems, and academic cancer centers, described by the company as covering roughly 2,100 care locations across the United States, Japan and Israel. Its Study Conduct platform uses source linked electronic case report forms with integrated source viewers, so monitors validate data against the originating clinical record. In April 2026 the company announced a research collaboration with the United States Food and Drug Administration under which trial data is analysed and key safety and efficacy signals are reported to sponsors and to the agency in near real time, already operational in a Phase 2 and a Phase 1b trial, with Amgen and AstraZeneca the first sponsors to join; the company describes itself as the sole technology provider operationalising the agency's Real-Time Clinical Trials proof of concept studies. It also announced a partnership with the contract research organisation Parexel in late 2025. Founded and led by Kent Thoelke, previously Chief Innovation Officer at ICON and Chief Scientific Officer at PRA Health Sciences; incubated by ARCH Venture Partners.

Ophthalmology company combining patented imaging hardware with machine learning and generative AI to standardize dry eye disease diagnosis, consolidating what are conventionally ten separate gold standard tests into a single exam of a few minutes and returning condition detection with personalized treatment recommendations for clinician approval. Montreal based and pre commercial, with a Quebec health agency subdivision assessing image quality and the accuracy of its diagnostic and treatment recommendation algorithms.

Point of care EEG company combining a rapidly deployable headband with the Clarity machine learning algorithm, which interprets EEG every ten seconds across all channels to produce a seizure burden estimate and alert bedside clinicians to suspected status epilepticus. Designed for emergency departments and intensive care units where conventional EEG and neurology interpretation are not immediately available. Publicly traded, and the only AI point of care EEG cleared across the full age range from preterm neonates through adults.

Radiologist founded company applying AI to detect peripheral vascular disease from CT imaging. Its VascularAssist Occlusion Triage software received FDA 510(k) clearance in June 2026 as a Class II computer aided triage and notification device, flagging suspected vascular occlusion in lower extremity CT for prioritized radiologist review, with the stated goal of catching limb threatening disease earlier. Clinical performance testing supporting the clearance is reported at 95 percent patient level sensitivity on two dimensional analysis and 94 percent on three dimensional, against a threshold of at least one lesion at 50 percent or greater stenosis. No specificity figure has been published alongside those numbers, which matters for a triage device and is discussed on the governance axis. The corporate position changed in mid 2026 and is worth understanding. The operating business is in Boston, but the listed entity is GuideAI Health Corp., a British Columbia corporation formed through a plan of arrangement completed on 16 June 2026, which filed a final non offering prospectus dated 12 June 2026 in British Columbia, Alberta and Ontario and began trading on Cboe Canada under the symbol GDAI at the end of June 2026. That makes the company a reporting issuer whose public filings sit on SEDAR+ rather than with United States securities regulators, which is where a diligence process should look for risk disclosure the website does not carry. Very early commercial stage, with a stated broader ambition in AI driven vascular care.

Clinical risk platform running real time machine learning models inside hospital EHRs to detect deteriorating patients, with early sepsis warning as the flagship use case and additional configured uses spanning clinical deterioration, pressure injuries, palliative care, and transitions of care. A Johns Hopkins spinout founded on roughly a decade of academic research, it is one of very few clinical AI vendors whose deployed system has been evaluated in large prospective multi site studies published in peer reviewed journals.

Dental AI platform that analyzes radiographs in real time, detecting and quantifying caries, periodontal bone loss, calculus, and other pathologies with visual overlays presented chairside. Founded by Harvard School of Dental Medicine and MIT alumni, the company holds multiple FDA clearances across detection and measurement claims and sells to dental groups and dental insurers, with a separate claims review product used on the payer side.

ECG led diagnostics software company whose AccurECG Analysis System is an FDA cleared Class II Software as a Medical Device for fully automated, near real time ECG interpretation. The platform is device agnostic, ingesting data from patches, Holter monitors, and telemetry devices, and performs beat by beat analysis, ectopic beat detection, heart rate measurement, and automated interpretation of thirteen rhythm classifications. A pipeline of ECG derived biomarkers is in development under FDA Breakthrough Device Designation.

Physician supervised virtual care company delivering asynchronous care through chat and voice messaging. Medical AI gathers context and provides initial evidence based guidance, and a board certified physician from an in house medical group licensed across all fifty states joins the conversation when diagnosis, prescription, or referral is required. Behind the interface sits a purpose built clinician cockpit and a self developed EMR designed for asynchronous care. Sold to payers, employers, and health systems as a front door to care.

Imaging AI company focused on prostate cancer and theranostics, built around PSMA PET/CT. Three products cover the pathway: DeepPSMA for automated lesion detection and whole body tumor burden quantification, SelectPSMA for predicting which patients will respond to PSMA targeted radioligand therapy, and TrackPSMA for automated longitudinal treatment response evaluation. Products are positioned for clinical and research decision support and are not FDA cleared.

Precision oncology clinical decision support company, spun out of Hungarian medical technology firm Oncompass Medicine, with offices in Cambridge and Budapest. Its Digital Drug Assignment platform analyzes a patient's tumor associated genetic alterations against research derived evidence and returns a ranked list of targeted therapies most likely to be effective, scoring each drug and genomic profile match. Positioned to support molecular tumor board recommendations rather than replace them.

Japanese health technology company whose AI symptom checker generates a personalized report on possible causes from a roughly three minute adaptive questionnaire. The algorithm is built on a stated corpus of more than 50,000 peer reviewed publications, supervised by a panel of over 50 physicians, and refined through feedback from a reported 1,700 healthcare provider organizations. Distinct from most consumer symptom checkers in publishing a preprint accuracy study rather than asserting accuracy.

Precision oncology company operating hospital linked genomics laboratories across India, Dubai, Nepal, and the Philippines, paired with OncoTwin, an AI clinical decision support platform that draws treatment insights from real world clinico genomic and outcomes linked data. Its stated thesis is that most genomic reference data represents Caucasian populations, and it builds population specific panels for South Asian and other underrepresented groups.

Oncology AI reading the full patient chart, founded 2021 on the observation that while software could already handle the roughly 20 percent of medical data that is structured, generative AI made the other 80 percent, the free text notes, tractable. OncoLLM is the underlying platform and the architecture is disclosed with unusual specificity: not one model but a constellation of eight specialized models ranging from 3 billion to 72 billion parameters, working agentically to interpret information at the patient level. Prism is the trial matching application, screening patients with upcoming appointments against all active trials and working in both directions, patient to trial and trial to patient, with continuous tracking. Matches are cited back to pathology, biomarkers, and note level evidence rather than returned as a bare recommendation. The platform has expanded beyond matching into verifiable patient summaries for visit preparation, cancer registry abstraction, and structured data generation for research. Reported results include a 40 percent increase in trial matches, more than 30 percent increase in enrollments, and 67 percent reduction in chart review time, with one NCI designated center reporting 100 percent screening coverage at three times the throughput of manual review. Peer reviewed validation has appeared in Nature Digital Medicine with presentation at ASCO, and a pilot study at the Medical College of Wisconsin Cancer Center covers gastrointestinal, genitourinary, breast, and thoracic teams. Mount Sinai Tisch Cancer Center deployed Prism in January 2026, becoming the first NCI designated Comprehensive Cancer Center in New York City to use it for systemwide matching. More than $36 million raised; co-founders Sarim Khan and Hrituraj Singh.

Note on naming: this is Artera of Los Altos, California (artera.ai), developer of multimodal AI cancer tests. It is unrelated to the separately indexed Artera of Santa Barbara (artera.io), which makes patient communication agents. The two companies share a name and nothing else. The ArteraAI Prostate Test is a multimodal artificial intelligence model combining digitized histopathology images from an existing biopsy with structured clinical data including age, PSA, and tumor stage, producing a continuous risk score from 0 to 1 with pre established cut points corresponding to roughly 3 percent and 10 percent estimated ten year risk of distant metastasis. It requires no additional procedure because it reads tissue already taken. Beyond prognosis it is predictive: for NCCN intermediate risk patients it estimates whether adding short term androgen deprivation therapy to radiation will reduce risk, which the literature describes as the first predictive biomarker for that decision. The algorithm is pre established and locked at version 1.2, developed from large datasets and multiple phase 3 randomized trials with up to 15 years of follow up. Regulatory and guideline standing is unusually complete: FDA De Novo marketing authorization in August 2025, inclusion in the NCCN Clinical Practice Guidelines for Prostate Cancer as the first and only AI risk stratification tool, and CMS coverage with a payment rate effective January 2024. In June 2026 the company introduced a digital pathology based test providing individual risk estimates in metastatic hormone sensitive prostate cancer. Co-founded by Felix Y. Feng of UCSF; CEO Andre Esteva.

AI clinical platform built for Latin America, and the first vendor in this index whose primary market is outside the United States and Europe. The platform combines ambient documentation, clinical decision support, and EHR integration: it transcribes consultations in real time and generates structured records adapted to each clinician's style, reviews the record, flags potential errors, and surfaces evidence based recommendations and institutional protocol guidance during the encounter. Models are trained on clinical guidelines, peer reviewed literature, and local institutional protocols, which is the substantive localization claim rather than translation alone. Since launching July 2025 the company reports deployment across more than 25 hospital systems in Brazil, Colombia, and Mexico, reaching 14 million patients and processing over 5 million consultations. Reported outcomes at customer sites include protocol adherence rising from 84 percent to 99 percent, physicians recovering roughly 1.7 to 2 hours daily, and 60,000 medical errors flagged in real time. Mater Dei in Brazil reports physicians using the platform around eight hours a day. A free tier targets independent private practice physicians who lack institutional technology access. The company positions the product as supporting clinicians rather than deciding, with the physician making the final call, and is operating while AI regulation is still forming regionally: Brazil's Senate has approved a risk based AI bill pending further approval, and Colombia has sent its own bill to Congress. Founded by Nicolas Abad after his father, a physician, died from a preventable drug interaction. $42 million raised including a $33 million Series A led by Andreessen Horowitz.

Indexed for what is commercially available today, which requires care because the company's own AI algorithms are not yet cleared. MyoVista Insights is a cloud based ECG reporting and management platform, in phased rollout since May 2025 and shipping at version 1.3 as of June 2026, positioned to replace on premise ECG management software. Its AI-ECG Algorithm Marketplace, introduced in version 1.3, integrates third party algorithm results into the platform, and the first algorithm made available is Bunkerhill Health's FDA cleared ECG-EF model detecting reduced left ventricular ejection fraction at or below 40 percent from a routine 12-lead ECG. Bunkerhill is separately indexed here, so this is a distribution relationship between two vendors in this index rather than a HeartSciences algorithm. The company's own pipeline is not yet available: the MyoVista wavECG device, a 12-lead resting ECG built to host AI-ECG algorithms, was submitted for 510(k) clearance in December 2025 and clearance was not confirmed at the time of this record; its impaired cardiac relaxation algorithm was deliberately separated from that submission to complete additional validation against revised American Society of Echocardiography guidelines for diastolic dysfunction; and its low ejection fraction algorithm remains in pre submission work. Underlying intellectual property is licensed in part from the Icahn School of Medicine at Mount Sinai. Assistive AI-ECG assessments are eligible for Medicare reimbursement under the Hospital Outpatient Prospective Payment System at APC 5734. NASDAQ: HSCS.

AI utilization management, and the only vendor in this index positioned to sit between payer and provider rather than serving one side. The Dragonfly platform (formerly CORTEX) uses real time predictive analytics to continuously assign an objective medical necessity score, the proprietary Care Level Score, and an anticipated level of care for every patient, with the stated intent of removing subjectivity from patient status determination so both parties work from the same evidence. Product lines cover Utilize for utilization review and case management, Navigate for discharge readiness and length of stay, Advise for physician advisor workflow and peer to peer escalation, and revenue integrity for denials and appeals. Precision UM is the deepest tier, a shared utilization management approach between a health system and a health plan built on common clinical data and historical determination patterns. Integration with the EMR is bidirectional. The company also sells Physician Advisor Services, supplementing night and weekend coverage or operating fully outsourced, so buyers should separate the platform from the staffed service. Named outcome: AnMed reported 14.6x return on investment over a twelve month period and 1,221 hours of front end review time saved through Precision UM with a national health plan. A third party study found Dragonfly rendered clinical review determinations 38 percent faster than fax and 15 percent faster than through the EMR. Headquartered in Franklin, Tennessee; co-founded by Joan Butters.

Indexed for the AI platform and agent portfolio rather than for the wider clinical research services, data, and consulting business, which is context under this index's product scoping rule. IQVIA (NYSE: IQV) launched IQVIA.ai in March 2026, a unified agentic platform and agent marketplace spanning clinical, commercial, and real world operations, built on the company's proprietary health data and Healthcare grade AI together with NVIDIA Nemotron, NeMo Agent Toolkit, Dynamo, and LangChain, following a collaboration begun over a year earlier. Disclosed agent use cases include target identification, clinical data review, literature review, market assessment, and healthcare professional engagement. The company reports filing more than 100 AI related patents and deploying more than 150 intelligent agents across internal teams and client environments, with 19 of the top 20 pharmaceutical companies having begun incorporating IQVIA agents into their workflows. IQVIA AI Assistant is a generative interface over the company's existing solutions and data products. The commercial side of the portfolio includes healthcare professional targeting and engagement, which sits closer to pharmaceutical marketing than to clinical operations and should be evaluated separately from the research and clinical agents.

Real world evidence generation, spun out of Stanford's Green Button project in 2020 and built to answer the question a clinician actually has at the bedside when no trial covers their specific patient. GENEVA OS is the underlying operating system across a federated network the company describes as exceeding 300 million anonymized patient records. ChatRWD is a chat based application that generates publication grade observational studies in minutes rather than months, without requiring a data scientist in the loop, and Green Button remains as a consultation service delivering evidence within 48 hours. Alexandria is the company's evidence library of pre computed evidence briefs. Two design decisions distinguish it: ChatRWD is an LLM independent framework, so an institution can maintain its own model security posture, and every answer carries a Real World Fitness Score rating how fit for purpose the underlying dataset was for that specific question, which is an explicit and unusual admission that evidence quality varies by question. Stanford Health Care has moved from using the service to embedding it in physician workflow and clinical notes. The Atropos Evidence Agent is distributed via the Databricks Marketplace under a partnership announced June 2025.

Clinical intelligence platform sold to health plans, spanning utilization management, payment integrity, appeals, care management, and policy management. The company states it is sold to health plans, and that providers and manufacturers do not license the platform, which makes it payer side alongside Alaffia. Cohere Unify combines a reported 350 or more clinically trained AI models with workflow automation and human review to auto determine prior authorization requests in real time, and Cohere Connect provides the prior authorization APIs, reported to have carried more than 15 million submissions and to support 47 million payer provider interactions annually. The critical design fact is the direction of automation: the company reports up to 85 percent real time approvals and states explicitly that remaining submissions are reviewed by a clinician before final determination, meaning the model approves and humans decide the rest. Additional products include Cohere Review Assist for acute inpatient care, Cohere Policy Studio, and a Payment Integrity Suite extended through the September 2025 acquisition of ZignaAI. Reported outcomes include care access 70 percent faster than traditional processes, up to 9x return on investment, 94 percent provider satisfaction, and a provider NPS of 67. Named plan relationships include Humana and Geisinger. $90 million Series C in May 2025.

Clinical AI platform built around Almanac Copilot, which the company describes as a Level 1 autonomous EHR agent: it retrieves and summarizes patient data, drafts notes, places orders, and surfaces prioritized alerts, dynamically selecting and chaining tools from a defined set including FHIR functions, a browser, clinical calculators, a Python interpreter, and the EHR database. As a Level 1 agent it acts only on explicit clinician command and requires review and approval of every action. Grounded in peer reviewed evidence in a vector database rather than an unconstrained model, and stated to be free of pharmaceutical advertising, a deliberate contrast with ad supported clinical reference products. On the EHR QA benchmark of 300 common EHR tasks it reported a 74 percent completion rate, matching much larger models. Founded by physician researcher Cyril Zakka MD, whose Stanford work introducing retrieval augmented generation to clinical medicine became one of NEJM AI's most cited papers. Important scope note carried from the source record: the company characterizes the platform as validated through research rather than shipped as a finished product, and it is undergoing clinical validation in academic medical center settings. $10 million seed in April 2026 led by F-Prime with General Catalyst and Lightspeed.

Clinical AI platform pairing ambient scribing with evidence grounded clinical reasoning, which the other scribes do not attempt. Clinicians describe a case in natural language and the platform returns a ranked differential with suggested workups, drafts a problem oriented assessment and plan with inline evidence links, and answers clinical questions with citations drawn from an index of more than 38 million peer reviewed articles, consensus guidelines, and FDA information covering over 154,000 drug compounds. In the Harvard and Stanford ARISE NOHARM evaluation, a physician validated clinical safety benchmark, the platform ranked among the top medical AI systems. The scribing side produces clinic notes, H&P notes, progress notes, discharge summaries, and patient handouts, with assisted EHR workflows across Epic, eClinicalWorks, athenahealth, and Elation. A Developer API exposes the full stack, including clinical question answering, chart summarization, differential generation, care planning, and scribing, so other platforms can embed the clinical intelligence. Independent testing notes reasoning is strongest on common presentations and weaker on complex multi system cases, and the company positions the product as decision support requiring physician judgment on every output. Founded 2021 by Dr Dereck Paul and Graham Ramsey.

Clinical AI for imaging triage, delivered through aiOS, an enterprise AI operating system that handles data normalization, continuous performance monitoring, and governance so health systems can run multi condition AI without re architecting infrastructure. In January 2026 the FDA cleared what the company describes as healthcare's first comprehensive AI triage solution, powered by CARE (Clinical AI Reasoning Engine), its self developed foundation model. The clearance brought 11 newly cleared indications together with three previously cleared into a single abdomen CT workflow, 14 in total, and per the FDA reviewed pivotal study the new indications reported a mean sensitivity of 97 percent and mean specificity of 98 percent, with the company reporting roughly an order of magnitude reduction in false alerts against single condition tools. The company reports more than 100 million patient cases analyzed on aiOS. A successor model, CARE2, has been announced, and a multi year collaboration with Amazon Web Services supports the foundation model work. Co founded and led by Elad Walach.

AI platform that retrieves, aggregates, and structures fragmented medical records into longitudinal patient histories, originally built for oncology and since extended to all therapeutic areas. The Clinical Clarity Engine applies natural language processing and machine learning to unstructured records; the company reports processing more than 300 million medical records from over 550,000 healthcare locations, with every structured output linked back to its source document. The xDECIDE provider portal presents structured data, autogenerated summaries, and AI assisted treatment option reports with scientific rationale, ranked by the xCORE engine and reviewed through a virtual tumor board; xINFORM is the patient facing portal. Data products supply longitudinal real world oncology datasets to research and regulatory programs. Holds HITRUST e1 certification. Raised a $46 million Series B in June 2026.

Agentic AI platform for health systems. Carebricks lets clinical and operational teams build, deploy, and govern their own AI agents across clinical, operational, and administrative workflows rather than buying a point solution per use case. Agents in production include coronary calcium detection on routine chest CT, nephrology triage, lung nodule follow up, referral prioritization, prior authorization packet assembly, and registry automation. The company develops its own FDA cleared imaging algorithms that run inside the platform, including algorithms for coronary artery calcium and aortic valve calcium on contrast enhanced non gated chest CT, mitral annular calcification, and bone mineral density. CMS established a national billing code and OPPS payment for algorithmic CAC and AVC analysis on chest CT effective April 1, 2026. In production at Cleveland Clinic, the University of Texas Medical Branch, Intermountain Health, and Mayo Clinic. Founded by Nishith Khandwala, previously a researcher at Stanford's Center for Artificial Intelligence in Medicine and Imaging.

Precision medicine company (NASDAQ: TEM) whose provider software is what this index covers: Next, an AI clinical decision support system that alerts clinicians to patients who may have fallen off care guidelines, backed by a vendor reported 662 patient prospective study across six sites; Hub, a physician platform rebuilt on an agentic architecture including a prior authorization agent; and David, a generative AI clinical assistant deployed directly into the EHR, with Northwestern Medicine as the first health system. Tempus acquired Deep 6 AI in March 2025, adding a precision research platform that applies natural language processing to structured and unstructured EMR data to match patients to clinical trials in near real time and to generate real world evidence. Deep 6 reports real time EMR feeds across more than 30 health systems, an ecosystem of over 1,000 research facilities including 18 academic medical centers and 11 NCI designated cancer centers, and that sites find more than 25 percent more patients than with traditional recruitment; the company notes 92 percent of trial inclusion and exclusion criteria benefit from unstructured data and that 15 to 20 percent of eligible patients are found through unstructured data alone. Outputs are positioned as decision support requiring trial team validation before action. The sequencing and pharma services businesses are context, not indexed products.

AI medical search and clinical decision support used at the point of care, free to verified United States clinicians and monetised through pharmaceutical and medical device advertising, with enterprise deployments embedding the platform inside Epic at named health systems. Two answer modes are offered: Quick Consult returns a short cited answer in seconds, and Deep Consult, introduced in mid 2025, runs a longer agentic synthesis returning a fuller report with more references. Answers are grounded in more than 300 medical journals plus FDA and CDC sources, under official AI partnerships with the New England Journal of Medicine, with JAMA and its eleven specialty journals, with Cochrane Systematic Reviews and with the National Comprehensive Cancer Network for treatment algorithms, alongside content agreements with more than a dozen specialty societies including the American College of Cardiology, the American Academy of Family Physicians and the American College of Emergency Physicians. Ancillary functions include patient handouts, clinical calculators and risk scores, and drafting prior authorisation letters. The company reports more than 757,000 verified clinician users, daily use by over 40 percent of practising United States physicians across more than 10,000 hospitals and medical centres, and approximately 20 million clinical consultations per month as of January 2026. Founded by Daniel Nadler, launched through the Mayo Clinic Platform Accelerate programme, and valued at 12 billion dollars following a Series D in January 2026. Material change in 2026: OpenEvidence terminated access across the European Union and the United Kingdom at the end of April 2026, citing mounting regulatory uncertainty regarding the treatment of AI systems in those markets including the EU Artificial Intelligence Act, so the product is now United States centred with primary verification built around the US National Provider Identifier. Unusually for this index, the platform has been the subject of a peer reviewed systematic review of eleven independent evaluations, which found it avoids fabricated citations and performs best on structured guideline based questions while accuracy varies on complex cases.