用AI打造多专业医疗模拟系统，提升团队协作能力
Designing and Evaluating an AI-enhanced Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education
- 融合大模型与虚拟引擎，实现角色互动与多模态交互
- 用户在真实临床情境中完成角色扮演，对话贴近实际
- 适合医学、护理等多专业学生实训，具可扩展性
跨专业医疗教育长期依赖案例分析和标准化病人，但传统方法受限于成本、规模及难以模拟真实临床复杂性。为此，我们设计并开发了AIMS（AI增强的沉浸式多学科模拟系统），整合Gemini-2.5-Flash大语言模型、Unity虚拟环境引擎及角色生成流程，支持用户与虚拟病人之间的同步多模态互动。AIMS旨在提升药学、医学、护理与社会工作专业学生的协同临床推理与健康促进能力。通过正式可用性测试，参与者以医疗团队成员身份，在脚本化与非脚本化对话中探索患者症状、社会背景与照护需求。测试发现音频路由、响应延迟等问题，并据此进行优化。结果表明，AIMS能支持真实、专业适配且情境恰当的对话。本文探讨了AIMS的技术创新，并展望未来发展方向。
原文摘要 · Abstract (English)
Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-enhanced Immersive Multidisciplinary Simulations), a virtual simulation that integrates a large language model (Gemini-2.5-Flash), a Unity-based virtual environment engine, and a character creation pipeline to support synchronized, multimodal interactions between the user and the virtual patient. AIMS was designed to enhance collaborative clinical reasoning and health promotion competencies among students from pharmacy, medicine, nursing, and social work. A formal usability testing session was conducted in which participants assumed professional roles on a healthcare team and engaged in a mix of scripted and unscripted conversations. Participants explored the patient's symptoms, social context, and care needs. Usability issues were identified (e.g., audio routing, response latency) and used to guide subsequent refinements. Findings suggest that AIMS supports realistic, profession-specific, and contextually appropriate conversations. We discuss technical innovations of AIMS and conclude with future directions.
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