ArticleCommunications medicine2026
Multidisciplinary blinded randomized expert evaluation of large language models for clinical diagnosis and management.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Patient education for neuromyelitis optica spectrum disorder using large language models: combining expert assessment and real-world patient interaction.Frontiers in physiology · 2026Article
- From risk classification to clinical action: a prespecified paired pilot benchmark of public large language model interfaces for diabetes-related foot ulcer prevention.Frontiers in endocrinology · 2026Article
Corrections and comments
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Authors and funding
44 authors.
Funding
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Abstract
backgroundDirect clinical uses of large language models (LLMs) remain controversial, partly because of the lack of methodological rigor in assessing their risks and benefits in medicine.
methodsWe developed Medieval, a multidisciplinary, randomized, and blinded expert evaluation framework. A ten-point Dreyfus-based scoring scale linked to career stages of human physicians was designed to reflect response qualities. Seven advanced LLMs or their distilled versions that were released within a short time-frame ( ≤ 45 days) in early 2025 were tested. Incidence of fabricated medical facts were documented. Linear mixed-effects models and variance-stabilizing Bayesian generalized linear mixed models were employed to perform statistical analyses.
resultsWe first develop a high-quality question bank comprising 685 real and simulated clinical cases across 13 specialties. An expert panel of 27 clinicians (average years of services: 25.9) evaluated the 4795 model responses. We show that these LLM ratings (n = 9856) have excellent reliability (intraclass correlation coefficients
conclusionsOur study shows that in spite of LLMs' substantial potentials in medicine, their unguarded clinical application could present serious risks, which must be continuously monitored by human expert panels. The evaluation framework developed and validated in this study will facilitate such efforts.
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Registered trials
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