Large language models encode clinical knowledge
Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We p…
Large language models encode clinical knowledge: We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias.
Historical evidence reading: the cited study may be limited by its design, population, period, or setting, and later research may report different effects.
Evidence
- Peer-reviewedNature Medicine2025-01-08
- Peer-reviewedDiagnostics2024-07-09
- Peer-reviewedJournal of Artificial Intelligence, Applications, and Innovations2024-01-01
- Peer-reviewedBioMedInformatics2026-03-13
- Peer-reviewedNature2023-07-12
Truvace Impact Record TRV-2026-0061, v6: “Large language models encode clinical knowledge.” Truvace, 2026-07-12. /record/TRV-2026-0061 (accessed at citation time). sha256 8a27d1970270ef45…
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