Concordance of five large language models with infectious diseases physicians on clinical decision-making: a multi-model comparative survey study.

purposeTo evaluate the accuracy of five large language models (LLMs) on a set of infectious diseases (ID) case-based multiple-choice questions and to compare their performance with that of practising ID physicians.

methodsA cross-sectional, web-based comparative survey was conducted among ID physicians and residents across Turkey. Ten original case scenarios (30 five-option multiple-choice questions; stratified by difficulty) were validated by a five-expert Delphi consensus panel (two rounds) using IDSA guidelines and EUCAST 2024 breakpoints as the reference standard. The same questions were put to five LLMs - Grok, ChatGPT-4o, Gemini 2.5, Claude Sonnet, and DeepSeek-V3 - via a standardised prompt in a single session. Primary outcomes were accuracy rate (%), Cohen's κ computed from observed response marginals, and an exact one-sided binomial test comparing each model's score against the observed physician-group accuracy, with Cohen's h as the effect-size measure for the difference between proportions.

resultsOf 129 respondents, 112 were included; 17 were excluded for self-reported AI use during the case section (this exclusion criterion was empirically supported: excluded participants scored significantly higher than included participants, 68.8% vs. 61.6%, Mann-Whitney p = 0.0015). Physician accuracy was 61.6% (18.5/30; SD 8.2; κ = 0.41 relative to a chance rate of 20%, interpreted descriptively). Four of five LLMs showed statistically significant superiority over the physician group: Grok 90.0% (κ = 0.806; Cohen's h = 0.69; binomial p < 0.001), ChatGPT-4o 83.3% (κ = 0.682; h = 0.50; p = 0.009), Gemini 2.5 83.3% (κ = 0.700; h = 0.50; p = 0.009), Claude Sonnet 80.0% (κ = 0.646; h = 0.41; p = 0.026); DeepSeek-V3 (76.7%; κ = 0.573; h = 0.33) did not reach statistical significance (p = 0.062). In Turkey-specific endemic cases, all models scored 100% versus 73% for physicians. Two items showed low concordance with the guideline-derived reference in both physicians and all LLMs; post-hoc review indicated that one of these items (prosthetic valve endocarditis) reflected a timing-dependent guideline nuance not fully captured by the original vignette, rather than a clear guideline violation. No correlation was found between physician AI-trust scores and clinical accuracy (Spearman ρ=-0.008; p = 0.937).

conclusionOn this case-based multiple-choice assessment, most evaluated LLMs achieved higher guideline concordance than a physician comparison group, particularly for guideline-standard and regionally endemic scenarios. These findings should be interpreted as evidence of accuracy on a structured test format rather than of general clinical decision-making superiority, and do not establish that LLMs should be used unsupervised in real clinical practice, which typically involves collaborative, multidisciplinary input rather than unilateral decisions.