TRV-2026-0518Certified recordPeer-reviewed

Performance of artificial intelligence in the collection of patient history in general practice

Automating administrative tasks, such as compiling a patient's medical history, could help general practitioners in their daily work. AI performance has improved in recent decades, but skepticism among professionals limits its use in medical practice, due to fears of gaps and biases. This study attempts to evaluate the effectiveness of AI in recording patient histories compared to general practitioners. Cross-sectional study. SITE: Online study in France. French general practitioners recruited online. We compare…

Health · Good Space — documented gain · certified 2026-07-23 · v5 · article view · machine-readable

Current reading — gain

AI collected patient histories more effectively than general practitioners during initial consultations, with stronger performance in older patients and for allergies and family history of cancer.

What this doesn’t fix

Effectiveness varied by patient and history type, with relevance and reliability not uniform across categories.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0518, v5: “Performance of artificial intelligence in the collection of patient history in general practice.” Truvace, 2026-10-14. /record/TRV-2026-0518 (accessed at citation time). sha256 4a28f5c30c30f2a8…

Calibration history

Every change to this record since certification, in the open.

  1. Sources changedv54a28f5c30c30…

    Source set updated

  2. Sources changedv4f608b83b03ff…

    Source set updated

  3. Sources changedv3344f3946b971…

    Source set updated

  4. Sources changedv2cd655bbe0b96…

    Source set updated

  5. Certifiedv1bf8e2ec650c5…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0518 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.