An interdisciplinary, randomized, single-blind expert evaluation of state-of-the-art large language models for their implications and risks in medical diagnosis and management

BackgroundState-of-the-art (SOTA) large language models (LLMs) are poised to revolutionize clinical medicine by transforming diagnostic, therapeutic, and interdisciplinary reasoning. Despite their promising capabilities, rigorous benchmarking of these models is essential to address concerns about their clinical proficiency and safety, particularly in high-risk environments. MethodsThis study implemented a multi-disciplinary, randomized, single-blind evaluation framework involving 27 experienced specialty clinicians with an average of 25.9 years of practice. The assessment covered 685 simulated and real clinical cases across 13 subspecialties, including both common and rare conditions. Evaluators rated LLM responses on medical strength (0-10 scale, where >9.5 signified leading expert proficiency) and hallucination severity (0-5 scale for fabricated or misleading medical elements). Seven SOTA LLMs were tested, including top-ranked models from the ARENA leaderboard, with statistical analyses applied to adjust for confounders such as response length. FindingsThe evaluation revealed clinical plausibility in general-purpose LLMs, with Gemini 2.0 Flash leading raw scores and DeepSeek R1 excelling in adjusted analyses. Top models demonstrated proficiency comparable to a physician of 6 years post qualification experience (score [~]6.0), yet significant risks were noted. Instances of incompetence (scores [≤]4) were detected across specialties, and 40 hallucination instances involving fabricated conditions, medications, and classification errors. These findings underscore the importance of implementing stringent safeguards to mitigate potential adverse outcomes in clinical applications. InterpretationWhile SOTA LLMs show substantial promise in enhancing clinical reasoning and decision-making, their unguarded application in medicine could present serious risks, such as misinformation and diagnostic errors. Human expert oversight remains crucial, particularly given reported incompetence and hallucination risks. Larger, multi-center studies are warranted to evaluate their real-world performance and track their evolution before broader clinical adoption.