协和医学杂志2026，Vol.17Issue(5)：1250-1255,6.DOI:10.12290/xhyxzz.2026-0716
通用大语言模型临床推理输出在医学教育中的适用性评估与比较
Applicability Assessment and Comparison of Clinical Reasoning Outputs from General-Purpose Large Language Models in Medical Education
摘要
Abstract
Objective To compare the diagnostic accuracy and differential diagnosis comprehensiveness of six general-purpose large language models(LLMs)on standardized clinical cases,and to assess their applicability for clinical reasoning education.Methods A total of 74 standardized clinical cases published in the BMJ"Endgames"column between March 2023 and February 2025 were included.Six general-purpose LLMs(ChatGPT-4,ChatGPT-4o,DeepSeek-V3,DeepSeek-R1,Gemini-2.0,and Kimi-k1.5)were prompted with identical text-only case information via their application programming interfaces(APIs).Diagnostic accuracy and differential diagnosis comprehensiveness were evaluated and compared across models.For statistical analysis,Cochran's Q test was used for overall comparisons of diagnostic accuracy,with post-hoc pairwise comparisons per-formed using McNemar tests;the Friedman test was used for overall comparisons of differential diagnosis compre-hensiveness,with post-hoc pairwise comparisons performed using Wilcoxon signed-rank tests;all P-values were adjusted using the Bonferroni method.Grade distribution differences were analyzed using Pearson's chi-square test.Results Diagnostic accuracy ranged from 62.2%to 78.4%across the six models,with a statistically signif-icant overall difference among models(P=0.022);however,no pairwise comparison remained significant after Bonferroni correction(all P＞0.05).Stratified analysis showed that accuracy was higher for cases without images(63.3%-81.6%)than for those with images(52.0%-72.0%),with no significant differences among models in either subgroup(all P＞0.05).Regarding differential diagnosis comprehensiveness,DeepSeek-R1 achieved the highest mean coverage rate(61.2%),while ChatGPT-4o scored the lowest(50.3%),with a statistically signifi-cant overall difference among models(χ2=16.6,P=0.005).Post-hoc pairwise comparisons revealed that ChatG-PT-4o was significantly inferior to Gemini-2.0(adjusted r=0.436,P=0.009).Regarding grade distribution,approximately 70%-80%of cases achieved only"moderate"or"limited"comprehensiveness,with no significant difference among models(χ2=17.90,P=0.268).Conclusions Current general-purpose LLMs can achieve a moderate level of diagnostic accuracy on standardized clinical cases,but demonstrate insufficient differential diag-nosis comprehensiveness.Given that diagnostic accuracy and differential diagnosis comprehensiveness are funda-mental elements of clinical reasoning education,these findings suggest that the quality of LLMs-generated outputs is not yet sufficient for direct application in teaching scenarios and should be used with caution under teacher su-pervision.关键词
人工智能/通用型大语言模型/医学教育/临床推理Key words
artificial intelligence/large language models/medical education/clinical reasoning分类
医药卫生引用本文复制引用
徐赫,翟林萱,朱亚伦,崔恒夫,朱祖懿,焦洋..通用大语言模型临床推理输出在医学教育中的适用性评估与比较[J].协和医学杂志,2026,17(5):1250-1255,6.基金项目
大学生创新创业项目(2025dcxm179) （2025dcxm179）
北京协和医院教育创新与改革项目(CMB-JYCXGG04) the National Undergraduate Innovation and Entrepreneurship Training Program(2025dcxm179) （CMB-JYCXGG04）
Peking Union Medical College Hospital Education Innovation and Reform Project(CMB-JYCXGG04) （CMB-JYCXGG04）
