用AI模拟病人对话，自动给医学生反馈，提升问诊能力。
MedSimAI: Simulation and Formative Feedback Generation to Enhance Deliberate Practice in Medical Education
- 基于大模型生成逼真医患对话，实时给出结构化反馈。
- 学生重复练习率达59.5%，部分学校问诊成绩平均提升6分（p<0.001）。
- 适合医学教育者推广使用，尤其需强化沟通与系统回顾训练。
医学教育在提供可扩展、一致的临床技能训练方面面临挑战。模拟标准化病人（SP）有助于提升沟通与诊断能力，但成本高且反馈质量不一。现有AI工具虽有潜力，却常缺乏全面评估框架、临床效果证据及自我调节学习（SRL）原则的整合。通过与医学教育专家协作的多阶段设计，我们开发了MedSimAI——一个依托大语言模型的AI仿真平台，支持通过互动患者对话实现刻意练习，并提供即时、结构化的反馈。在三所医学院的多机构部署中（410名学生，1,024次交互），59.5%的学生进行了重复练习。一所机构的客观结构化临床考试（OSCE）问诊得分从82.8升至88.8（p < 0.001，Cohen's d = 0.75）；另一所试点未见显著变化。自动化评分在识别主访谈评分量表（MIRS）熟练度阈值上达到87%准确率。混合效应分析显示机构与病例差异。对840份学习反思的定性分析指出，学生主要存在遗漏项目、条理不清、系统回顾不足和共情缺失等问题。这些发现表明，MedSimAI可作为问诊与沟通的可扩展形成性训练平台，推动分阶段课程整合与高级学习者的真实性提升。
原文摘要 · Abstract (English)
Medical education faces challenges in providing scalable, consistent clinical skills training. Simulation with standardized patients (SPs) develops communication and diagnostic skills but remains resource-intensive and variable in feedback quality. Existing AI-based tools show promise yet often lack comprehensive assessment frameworks, evidence of clinical impact, and integration of self-regulated learning (SRL) principles. Through a multi-phase co-design process with medical education experts, we developed MedSimAI, an AI-powered simulation platform that enables deliberate practice through interactive patient encounters with immediate, structured feedback. Leveraging large language models, MedSimAI generates realistic clinical interactions and provides automated assessments aligned with validated evaluation frameworks. In a multi-institutional deployment (410 students; 1,024 encounters across three medical schools), 59.5 percent engaged in repeated practice. At one site, mean Objective Structured Clinical Examination (OSCE) history-taking scores rose from 82.8 to 88.8 (p < 0.001, Cohen's d = 0.75), while a second site's pilot showed no significant change. Automated scoring achieved 87 percent accuracy in identifying proficiency thresholds on the Master Interview Rating Scale (MIRS). Mixed-effects analyses revealed institution and case effects. Thematic analysis of 840 learner reflections highlighted challenges in missed items, organization, review of systems, and empathy. These findings position MedSimAI as a scalable formative platform for history-taking and communication, motivating staged curriculum integration and realism enhancements for advanced learners.
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