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.

翻译：医学教育在提供可扩展、一致的临床技能培训方面面临挑战。使用标准化病人（SPs）的模拟训练可培养沟通与诊断技能，但其资源密集且反馈质量参差不齐。现有基于人工智能的工具虽展现出潜力，但往往缺乏全面的评估框架、临床影响的证据以及自我调节学习（SRL）原则的整合。通过与医学教育专家进行多阶段协同设计，我们开发了MedSimAI——一个由人工智能驱动的模拟平台，该平台通过交互式病人接诊场景及即时、结构化的反馈，实现了刻意练习。MedSimAI利用大语言模型生成逼真的临床互动，并提供与经过验证的评估框架相一致的自动化评估。在一次多机构部署中（涉及三所医学院的410名学生，共1024次接诊），59.5%的学生参与了重复练习。在一个试点机构，客观结构化临床考试（OSCE）病史采集的平均分数从82.8分上升至88.8分（p < 0.001，Cohen's d = 0.75），而第二个试点机构的初步结果显示无显著变化。在依据主访谈评定量表（MIRS）识别能力阈值方面，自动化评分达到了87%的准确率。混合效应分析揭示了机构和病例的影响。对840份学习者反思的主题分析突显了其在遗漏项目、组织性、系统回顾和共情方面的挑战。这些发现确立了MedSimAI作为一个可扩展的、用于病史采集和沟通技能的形成性训练平台，并推动了针对高阶学习者的分阶段课程整合与真实性增强。