用AI虚拟病人在VR中模拟问诊，让医学生低成本练沟通
CLiVR: Conversational Learning System in Virtual Reality with AI-Powered Patients
- 用大模型+语音+3D avatar实现自然对话式问诊
- 13位专家评估显示教学效果高、真实感强
- 适合医学院培训，比真人患者更易扩展
模拟训练是医学与护理教育的核心环节，传统依赖标准化病人（SP）和高保真模拟人，但成本高、难推广。本文提出CLiVR——一种基于虚拟现实的对话式学习系统，融合大语言模型（LLMs）、语音处理与3D角色，实现医生与虚拟患者的自然交互。系统基于症状-综合征数据库动态生成对话内容，并通过情感分析反馈沟通语气。开发于Unity，部署于Meta Quest 3平台。通过包含13名医学院教师的专家用户研究，评估了可用性、真实感与教学潜力，结果表明用户接受度高，对教育价值信心充足，并提供了改进意见。该系统为标准化病人训练提供了一种可扩展、沉浸式的补充方案。
原文摘要 · Abstract (English)
Simulations constitute a fundamental component of medical and nursing education and traditionally employ standardized patients (SP) and high-fidelity manikins to develop clinical reasoning and communication skills. However, these methods require substantial resources, limiting accessibility and scalability. In this study, we introduce CLiVR, a Conversational Learning system in Virtual Reality that integrates large language models (LLMs), speech processing, and 3D avatars to simulate realistic doctor-patient interactions. Developed in Unity and deployed on the Meta Quest 3 platform, CLiVR enables trainees to engage in natural dialogue with virtual patients. Each simulation is dynamically generated from a syndrome-symptom database and enhanced with sentiment analysis to provide feedback on communication tone. Through an expert user study involving medical school faculty (n=13), we assessed usability, realism, and perceived educational impact. Results demonstrated strong user acceptance, high confidence in educational potential, and valuable feedback for improvement. CLiVR offers a scalable, immersive supplement to SP-based training.
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