针对重症医学研究生教育中存在的教学资源不足、理论与实践脱节等突出问题，探索基于人工智能（AI）的教学创新路径，提升研究生临床思维与应急处置能力。

天津医科大学总医院重症医学科组建研究团队，筛选2019年至2023年临床真实典型病例50例（覆盖重症医学全部核心病种），经双盲脱敏后借助AI改写为适配研究生培养的教学案例，按难度分为初级（基础病例）、中级（多学科协作病例）、高级（疑难/技术创新病例）；构建知识图谱以实现案例的动态生成与个性化推送，搭建集成前测分层学习、虚拟急救场景模拟、思维导图对比专家决策等功能的智能教学平台，采用“课前预习—课中模拟—课后反思”三阶段教学流程。以天津医科大学2022~2024级33名重症医学专业研究生为实验组（采用该智能平台 + 新型教学模式），2021~2023级34名同专业研究生为对照组（采用传统教学模式），两组基线资料差异无统计学意义（P＞0.05），具有可比性。观察指标包括结课考试病例分析题成绩、学生对教学模式的满意度及能力提升评价问卷调查结果。

实验组结课考试病例分析题平均成绩为75.94分，显著高于对照组的65.64分，提升幅度达10.30分；问卷调查显示，81.8%（27/33）的实验组学生认为该模式有助于深化理论理解与应用，90.9%（30/33）认为其提升了临床决策与应急能力，87.9%认为其有助于提升医学人文素养，87.9%表示愿意继续使用该平台自学。

基于AI的智能案例平台通过系统化案例设计、分层适配教学、个性化推送及三阶段教学流程，可显著提升重症医学研究生的临床案例分析能力、临床决策与应急处置能力，获得学生广泛认可，为重症医学研究生教育提供了切实有效的教学新范式，具备向其他临床学科推广的潜力。

To address the prominent issues in graduate education in critical care medicine, such as insufficient teaching resources and the disconnection between theory and practice, this study explores an innovative teaching approach based on artificial intelligence (AI) to enhance graduate students' clinical reasoning and emergency response capabilities.

A research team was established at the Department of Critical Care Medicine, Tianjin Medical University General Hospital. Fifty real and typical clinical cases from 2019 to 2023, covering all core diseases in critical care medicine, were selected. After double-blind desensitization, these cases were transformed into teaching cases suitable for graduate training using AI and classified into three difficulty levels: elementary (basic cases), intermediate (multidisciplinary collaborative cases), and advanced (difficult/technologically innovative cases). A knowledge graph was constructed to enable dynamic case generation and personalized case recommendations, and an intelligent teaching platform was built, integrating functions such as pre-test-based stratified learning, virtual emergency scenario simulation, and mind mapping for comparing with expert decision-making. A three-stage teaching process of "pre-class preparation—in-class simulation—post-class reflection" was adopted. Thirty-three graduate students majoring in critical care medicine from the 2022–2024 cohorts of Tianjin Medical University were selected as the experimental group (using the intelligent platform + novel teaching model), and 34 graduate students of the same major from the 2021–2023 cohorts served as the control group (adopting the traditional teaching model). There were no statistically significant differences in baseline data between the two groups (P＞0.05), indicating comparability. The observation indicators included the scores of case analysis questions in the final examination, and the results of a questionnaire survey on students' satisfaction with the teaching model and evaluation of ability improvement.

The average score of the experimental group on case analysis questions was 75.94, which was significantly higher than that of the control group (65.64), with an improvement of 10.3 points. The questionnaire survey results showed that 81.8% (27/33) of the experimental group students believed the model facilitated the deepening of theoretical understanding and application, 90.9% (30/33) reported enhanced clinical decision-making and emergency response capabilities, 87.9% thought it helped improve medical humanistic literacy, and 87.9% expressed willingness to continue using the platform for self-directed learning.

Through systematic case design, hierarchical adaptive teaching, personalized case recommendations, and a three-stage teaching process, the AI-based intelligent case platform can significantly improve critical care medicine graduate students' clinical case analysis ability, clinical decision-making, and emergency response capabilities. Widely recognized by students, it provides an effective new teaching paradigm for graduate education in critical care medicine and has the potential to be promoted to other clinical disciplines.