TRV-2026-0061Version 6 · Revised
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TRUVACE RECORD VERSION record: TRV-2026-0061 version: 6 kind: revised reason: Reading revised timestamp: 2026-07-12T20:58:06.798434Z status: published lens: p_space sector: health headline: Large language models encode clinical knowledge dek: 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… gain_reading: (none) problem_reading: 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. limitation: Historical evidence reading: the cited study may be limited by its design, population, period, or setting, and later research may report different effects. tag: Evidence-backed problem key_points: 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. rundown: 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 propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. sources: - peer_reviewed | BioMedInformatics | https://doi.org/10.3390/biomedinformatics6020013 | 2026-03-13 - peer_reviewed | Diagnostics | https://doi.org/10.3390/diagnostics14141468 | 2024-07-09 - peer_reviewed | Journal of Artificial Intelligence, Applications, and Innovations | https://doi.org/10.61838/jaiai.1.4.5 | 2024-01-01 - peer_reviewed | Nature | https://doi.org/10.1038/s41586-023-06291-2 | 2023-07-12 - peer_reviewed | Nature Medicine | https://doi.org/10.1038/s41591-024-03423-7 | 2025-01-08 prev: 440d828c21cc570494b90129e4857728f0bc3f7c737e9b881315e1a232e82bbd
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