Dyania Health

A medically trained large language model does work that previously required a clinician reading a chart. The company is precise about what the system does and does not do: it answers specific clinical questions to find signals in a patient's history rather than generating summaries, which its CEO states explicitly. Reported throughput is the clearest evidence of centrality, processing structured and unstructured record data in under half a second against 30 minutes to two hours for a human reviewer.

Clinician in the loop is built into the deployed workflow and was preserved even in the published study. The peer reviewed evaluation describes clinical team validation as an essential component of the workflow to ensure safety and accuracy, with the AI pre screening and humans confirming. The company has also published research on stepwise confidence estimation for failure detection in self evaluating multi step LLM tasks, which is unusually direct engagement with the question of when a model should flag its own uncertainty rather than assert an answer.

Among the most transparent in the index because the company publishes methods rather than claims. Its approach is described as a medically trained LLM based end to end system built on a large annotated medical record dataset developed since 2020, with reported accuracy above 95 percent. Multiple peer reviewed and preprint publications describe the system, including evaluations for cardiac amyloidosis trial screening and a phase 3 polycythemia vera study, plus methodological work on confidence estimation. An external reviewer can read the methods and assess them independently.

Peer reviewed real world evidence at a major academic health system, which very few vendors in this index can claim. A study with a major academic medical center published in a Heart Failure Society journal evaluated the system pre screening for a Phase III transthyretin amyloid cardiomyopathy trial: in one week it reviewed 1,476 patients and identified 46 potential matches, and 29 of the 30 confirmed matches had not been found through existing methods. Separately reported results include 35 percent more diverse patients identified than manual review and 97 percent of identified patients missed by prior manual screening. The health system moved to enterprise wide rollout after a year of work in cancer and cardiovascular disease. Figures beyond the published study remain vendor reported.

No published PHI framework or data governance disclosure was located. The system was embedded within a health system EMR screening across 25 hospitals and 250 outpatient centers in three states, which is a very broad data surface, and the company's model was trained on a large annotated medical record dataset whose provenance and consent basis are not described publicly.

No HIPAA or BAA commitment was located in public materials, though business associate status is structurally required given EMR embedded deployment at United States health systems, with human subjects research protections layered on top for the trial screening use case.

No SOC 2, HITRUST, or ISO 27001 attestation was located, and no trust center was found.

No FDA pathway is claimed. Automated chart abstraction and trial pre screening identify candidates and extract documented information for human confirmation rather than informing a treatment decision, which sits outside device regulation. The governing regime is human subjects research oversight for the trial applications, and the published study's emphasis on clinical team validation reflects that framing.

Better engagement than almost any vendor in the index, and it is measured rather than asserted. The published research explicitly frames improved equity of trial enrollment as an outcome alongside speed and accuracy, and the company reports identifying 35 percent more diverse patients than manual chart review, with its CEO stating the goal includes surfacing eligible patients from historically underrepresented groups who would otherwise be missed. Separate published work on stepwise confidence estimation for failure detection addresses the model reliability side. Short of an A only because there is no standing governance program or ongoing monitoring commitment published.

The system was embedded within a major academic health system's EMR and screened data across 25 hospitals and 250 outpatient centers spanning three states, continuously re reading dynamically changing records to catch patients who become eligible over time. Running daily against a live record set at that footprint is a substantially harder integration than a batch extract, and the company states it handles deployment and ongoing maintenance itself as a one time implementation.

No hosting, tenancy, or data residency terms were located. The company has offices in New Jersey and Greece, so processing location is a question worth raising directly.

No pricing is published, but the company frames the economic comparison unusually concretely: it states health systems with roughly 500,000 patients spend more than 5 million dollars annually on staff performing manual chart abstraction, and positions savings of up to 50 percent of that cost. Naming the baseline spend and a percentage against it gives a buyer a modelable frame even without a rate, which is more than most vendors offer. Deployment is described as a one time implementation with the company handling ongoing maintenance.

Described as disease agnostic with demonstrated depth in cardiology and oncology, extending to autoimmune conditions, and applied across clinical trial pre screening, observational studies, registry population and reporting, therapeutic gap identification, and quality measurement. Buyers span health systems, physicians, pharmaceutical sponsors, and researchers. The published evidence concentrates in cardiology, specifically rare disease trial screening, so breadth beyond that should be read as platform capability rather than demonstrated performance.