协和医学杂志2026，Vol.17Issue(5)：1242-1249,8.DOI:10.12290/xhyxzz.2026-0759
人工智能驱动的全链路临床诊断思维教学模式研发与评价
Development and Evaluation of an Artificial Intelligence-Driven Full-Link Teaching Model for Clinical Diagnostic Thinking
摘要
Abstract
Objective To retrospectively summarize the clinical teaching application of our team's self-developed artificial intelligence(AI)full-chain clinical diagnostic reasoning training teaching agent,observe the differences in teaching outcomes among medical students under different teaching models,and preliminarily analyze the application trends and potential value of the Plan-Do-Check-Act(PDCA)management mechanism in AI-assisted clinical teaching,so as to provide reference practical data and experiential insights for the optimi-zation and improvement of"AI+clinical medical teaching".Methods This study was a retrospective observa-tional study that enrolled clinical medicine students who had completed the Fundamentals of Diagnostics course at Shanghai Jiao Tong University School of Medicine from September 2024 to September 2025 as the study sub-jects.Based on the established teaching reform implementation plan,the participants were divided into three groups:traditional teaching group(Group A),blended AI teaching group(Group B),and PDCA cycle-opti-mized blended AI teaching group(Group C).The three groups maintained essential consistency in teaching syl-labi,core content,and total class hours(32 credit hours).Group A received conventional lecture-based class-room instruction and ward-based small-group mentoring.Group B received traditional teaching plus AI-assisted instruction using the AI full-chain clinical diagnostic reasoning training system,but without implementing PDCA management.Group C received traditional teaching plus AI-assisted instruction with PDCA management,i.e.,a full-cycle teaching quality management closed-loop system based on conventional teaching and AI train-ing.Baseline assessment data before the course,final theoretical examination scores in Basic Diagnostics,and objective structured clinical examination(OSCE)clinical skills assessment data after course completion were collected across the three groups to compare the differences in teaching effectiveness among different teaching models.Results A total of 180 clinical medicine students were enrolled,with 60 students in each of Groups A,B,and C.No statistically significant differences were observed among the three groups in baseline data,in-cluding age,gender,previous grade point average,pre-internship diagnostic theory test scores,and doctor-pa-tient communication assessment scores(all P＞0.05),indicating comparability of baseline data between groups.Post-course assessments showed that the final theoretical examination scores in Basic Diagnostics for Groups A,B,and C were(83.1±5.9),(83.4±6.0),and(82.6±5.4),respectively,with no statistically significant difference between groups(P=0.739).In terms of OSCE clinical skills assessment,Groups B and C scored higher than Group A in both the"history taking"module[(16.0±0.7)and(17.2±0.9)vs.(14.4±0.8),respectively]and the"case analysis"module[(14.9±0.7)and(16.0±0.7)vs.(13.0±0.8),respectively].Furthermore,Group C outperformed Group B in both modules.All between-group differ-ences were statistically significant(all P＜0.017).Conclusions Compared with the traditional teaching mod-el,the teaching protocol incorporating our self-developed AI full-chain teaching tool,combined with the PDCA closed-loop management composite teaching model,may help improve medical students'practical abilities in clinical diagnostic reasoning,without exerting negative interference on their mastery of theoretical knowledge.The teaching format integrating AI empowerment with PDCA closed-loop optimization may have potential effects in enhancing the stability and homogenization level of clinical teaching outcomes.It may serve as an optional im-provement strategy for clinical diagnostic reasoning teaching.However,its long-term effectiveness and broader applicability still require further validation.关键词
人工智能/临床诊断思维/PDCA循环/混合式AI教学/医学教育Key words
artificial intelligence/clinical diagnostic reasoning/PDCA cycle/hybrid AI teaching/medical education分类
医药卫生引用本文复制引用
陈胤儒,陈孜瑾,黄飞跃,龚震晔,顾倩,顾卫琼..人工智能驱动的全链路临床诊断思维教学模式研发与评价[J].协和医学杂志,2026,17(5):1242-1249,8.基金项目
上海高校市级一流本科课程《临床诊断思维》(沪教委高[2025]5号) （沪教委高[2025]5号）
上海高校市级重点课程《临床诊断思维》(沪教委高[2024]38号) （沪教委高[2024]38号）
上海市2024年度教育科学研究规划项目"基于人工智能和数字孪生技术的临床医学专业实习生能力评价体系建设"(C2024050) Shanghai Municipal First-Class Undergraduate Course of Shanghai Institutions of Higher Learning《Clinical Diagnostic Thinking》(Shanghai Municipal Education Commission Higher Education Document No.[2025]5) （C2024050）
Shanghai Municipal Key Course of Shanghai Institutions of Higher Learning《Clinical Diagnostic Thinking》(Shanghai Municipal Education Commission Higher Educa-tion Document No.[2024]38) （Shanghai Municipal Education Commission Higher Educa-tion Document No.[2024]38）
Shanghai 2024 Educational Science Research Planning Project"Construction of Competency Evalua-tion System for Clinical Medical Interns Based on Artificial Intelligence and Digital Twin Technology"(C2024050) （C2024050）
