aceSequential diagnosis with language models
We explored the capabilities of frontier language models in sequential diagnosis, a diagnostic paradigm that captures the iterative nature of clinical reasoning. Given a brief clinical presentation, clinicians selectively acquired information to reach a diagnosis, enabling assessment of diagnostic accuracy and workup cost. We developed an interactive benchmark and an artificial intelligence (AI) system that simulates a panel of physicians, continually revising the differential diagnosis and selecting cost-effect…
An AI system that simulates a panel of physicians and selects tests reached higher diagnostic accuracy than generalist physicians on two benchmark case sets.
The authors state the findings were obtained in a controlled benchmark setting, so they do not directly show performance or cost savings in real clinical practice.
Evidence
- Peer-reviewedScience2026-10-22
How should this claim be treated?
Truvace Impact Record TRV-2026-2234, v1: “Sequential diagnosis with language models.” Truvace, 2026-10-24. /record/TRV-2026-2234 (accessed at citation time). sha256 5446d11e84eeae44…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2234 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.