aceConcordance of five large language models with infectious diseases physicians on clinical decision-making: a multi-model comparative survey study
Purpose To 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. Methods A 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 guidel…
On a validated 30-question infectious diseases case test, four of five language models scored significantly higher than the physician group's 61.6% accuracy, with Grok reaching 90.0%.
The authors say the results show accuracy on a structured multiple-choice test, not general clinical decision-making superiority, and do not establish that models should be used unsupervised in real practice.
Evidence
- Peer-reviewedInfection2026-10-06
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Truvace Impact Record TRV-2026-1797, v1: “Concordance of five large language models with infectious diseases physicians on clinical decision-making: a multi-model comparative survey study.” Truvace, 2026-10-18. /record/TRV-2026-1797 (accessed at citation time). sha256 988a5e667c8ce884…
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