Aidoc vs HOPPR (2026)
Two platforms at different altitudes, and putting them side by side is the point. Aidoc is what a health system buys: an operating system running detection models across an imaging estate with normalization, monitoring and governance, plus its own clearances. HOPPR is what the companies building those models buy, a multimodal foundation model and development environment for imaging AI, with detailed model disclosure and a stated consent basis for its training data. Nobody chooses between them. What the pair shows is that a hospital evaluating imaging AI is increasingly evaluating a stack, and the foundation layer where representativeness is decided is the one nobody in the purchasing chain can see.
- It runs detection models across an imaging estate through an operating system handling normalization, monitoring and governance for every model on it.
- The 2026 clearance covers many acute indications from a single named foundation model, with regulator reviewed performance behind it.
- For a health system, the platform is the durable purchase rather than any individual algorithm.
- It is the layer beneath the applications, a multimodal foundation model and development environment sold to developers, AI companies and imaging vendors.
- It discloses its model and technology in detail, which matters when others build regulated products on it.
- The consent basis for training data is stated rather than implied, which almost nothing in this lane does.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Aidoc and HOPPR are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
Side by Side
Select any grade to read the note behind it.
Product updates
The latest logged changes from each vendor, newest first, each linked to its source.
Aidoc
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Regulatory / FDA
Aidoc received FDA Breakthrough Device Designation for CARE Chest X-Ray Triage, built on its CARE foundation model, covering triage of pneumomediastinum and lobar or lung collapse.
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Clinical evidence
Mercy and Aidoc released a white paper, commissioned by AVIA, reporting on five months of AI deployment across the Mercy health system.
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Regulatory / FDA
The FDA cleared Aidoc's comprehensive abdomen CT triage solution, powered by its self developed CARE foundation model, bringing 11 newly cleared indications together with three previously cleared into a single 14 indication workflow.
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HOPPR
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Model / architecture
HOPPR expanded its vision-language model portfolio with the release of a new 2D Mammography Narrative Model.
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Each vendor in one paragraph
Written to be quoted whole. Each paragraph opens the AI Health Index profile of the vendor: what the product is, who buys it and what it does.
Aidoc sells clinical AI for imaging triage and follow up to health systems, delivered through aiOS, an enterprise platform that runs Aidoc's own FDA cleared algorithms alongside vetted partner algorithms and carries care coordination and patient management. Aidoc Medical was established in early 2016 in Tel Aviv and is led by cofounder and chief executive Elad Walach.
Source: AI Health Index, August 2026
HOPPR builds infrastructure for making medical imaging AI and, separately, a layer that delivers AI results into radiology reporting. Grace is a multimodal imaging foundation model for X-ray, CT, MRI and echocardiography, trained on more than a petabyte of permission based, anonymized studies paired with reports; narrative models translate chest radiographs (April 2026) and 2D mammograms (August 2026) into structured text and ship with training data traceability records and version locking.
Source: AI Health Index, August 2026
Questions buyers ask
How are Aidoc and HOPPR priced and sold?
Aidoc sells aiOS and its cleared modules to health systems under enterprise agreements through a demo, and maintains an AWS Marketplace listing, which makes the procurement channel public; the listing states that pricing depends on the duration and terms of the contract agreed with the vendor. Whether pricing runs per module, per study volume or per site, and how partner algorithms on aiOS are billed, is not stated.
With HOPPR, the Foundry is sold as platform access to developers, AI companies and imaging software vendors through an access request, with Forward Deployed Services as a separate professional services engagement, and Presto Agent is sold separately to radiology practices. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified August 4, 2026. No vendor pays for placement.
How do Aidoc and HOPPR handle patient data?
In Aidoc's case, the company's security and privacy documentation describes a new DICOM object created before upload carrying only non PHI data and a newly generated identifier; analysis runs on that object, and the identifier re-attaches PHI locally when results return.
HOPPR describes Grace's training corpus as permission based and anonymized, with de identification at ingestion, and the Foundry is covered by SOC 2 Type II attestation and HITRUST e1 certification. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified August 4, 2026. No vendor pays for placement.
Which settings and specialties do Aidoc and HOPPR cover?
With Aidoc, the company reports use in more than 1,600 medical centers and names health systems including WellSpan, Mercy, Yale New Haven, St. Luke's and Renown. Validation sits at the level of each cleared indication, with its own FDA review, and the research library is organized by condition.
In HOPPR's case, the Foundry is sold to developers, AI companies and imaging software vendors, and Presto Agent to radiology practices, with named practices using it. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified August 4, 2026. No vendor pays for placement.
How much do Aidoc and HOPPR leave to the clinician?
Aidoc flags suspected acute findings, reorders the worklist and pushes a notification with a preview image to a desktop widget; results can also go to the native worklist, the reporting system or PACS as a separate series. The K252970 indication states the limits: analysis runs in parallel with the standard read, preview images are for notification only, the device is not for diagnosis, and notified clinicians must view the full images.
With HOPPR, the radiologist signs the report, but nothing published states what Presto may insert without confirmation, whether inserted content is marked as model generated at the point of signing, or whether the signed report records which model produced which content. A number placed in a template looks the same whether right or wrong, and checking it means remeasuring, which is the work the feature removes. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified August 4, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers.
These are not alternatives, since one is bought by a health system and the other by the companies building what health systems buy, and the comparison exists to make that layering visible. The risk at the foundation layer is different in kind: a model trained on an unrepresentative corpus carries that variation into every downstream product, and neither the developer nor the eventual hospital buyer will see where it came from. HOPPR publishes no subgroup analysis or model card describing training data composition, and neither vendor publishes pricing.