ArticleBMC medical education2026
Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundLarge language models are increasingly used as supportive tools in medical education; however, their reliability in anatomically based clinical reasoning remains insufficiently defined. This study aimed to compare the accuracy, depth, and clinical applicability of model-generated responses to thoracic anatomy–based clinical scenarios and to assess inter-rater reliability among expert anatomists.
methodsThis exploratory comparative study evaluated four widely used models—ChatGPT-4o, DeepSeek-V2, Gemini, and Grok—using 20 open-ended thoracic anatomy questions derived from seven clinical scenarios adapted from Moore’s Clinically Oriented Anatomy. Each model generated responses without modification. Two expert anatomists independently assessed all responses using a standardized 10-point scoring system based on anatomical accuracy and clinical relevance. Inter-rater reliability was analyzed using the Intraclass Correlation Coefficient (ICC). Differences in model performance were examined using the Kruskal–Wallis test, with p < 0.05 considered statistically significant.
resultsInter-rater reliability was excellent across all models (ChatGPT-4o: ICC = 1.000; DeepSeek-V2: ICC = 1.000; Gemini: ICC = 0.956; Grok: ICC = 0.977). Significant differences in performance were observed among the models (p < 0.05). Post-hoc analysis demonstrated that Grok achieved the highest median score (7.75), significantly outperforming ChatGPT-4o (4.0), DeepSeek-V2 (4.5), and Gemini (3.0), which showed comparable performance.
conclusionsLarge language models demonstrate variable yet promising potential in supporting clinically oriented thoracic anatomy education. While some models provide more accurate and contextually appropriate responses, expert oversight remains essential. Understanding model-specific strengths and limitations is critical for the safe and responsible integration of AI into biomedical education.
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