AI智能体在国内医学教育领域的研究进展

Research Progress of AI Agents in the Field of Domestic Medical Education

摘要: AI智能体作为人工智能技术十分重要的分支，正深刻地改变着医学教育的传统模式。本综述从功能分类、应用领域、技术实现以及发展趋势多个维度出发，对AI智能体在医学教育领域的研究进展进行了系统性的梳理和分析。研究表明，教学辅助型智能体能通过个性化学习路径设计和实时答疑以此提升学习效率；评估反馈型智能体可对学习效果做多维、过程性的客观评价；资源整合型智能体则构建了跨学科的知识网络。在基础医学教育中，智能体解决了传统教学互动不足的问题；在临床技能培训方面，智能体借助虚拟仿真和语音交互技术，提升了学生的实践能力；在康复与护理领域中，智能问答系统等应用有效支持了患者教育与护理人员在职培训。技术支撑方面，自然语言处理、机器学习模型和多模态交互构成了核心支撑。未来研究将聚焦跨学科融合、智能化升级和全球化合作，但仍需应对数据隐私、技术局限和评价体系不完善等挑战。

Abstract: As a very important branch of artificial intelligence technology, AI agent is profoundly changing the traditional mode of medical education. This review systematically combs and analyzes the research progress of AI agents in the field of medical education from many dimensions of functional classification, application fields, technical implementation and development trends. The research shows that teaching-assisted intelligent agents can improve learning efficiency through personalized learning path design and real-time Q&A. Evaluation feedback agent can make multi-dimensional and process objective evaluation of learning effect. The resource integration agent constructs an interdisciplinary knowledge network. In basic medical education, agents solve the problem of insufficient interaction in traditional teaching. In the aspect of clinical skills training, the agent improved students’ practical ability with the help of virtual simulation and voice interaction technology. In the field of rehabilitation and nursing, applications such as intelligent question answering system effectively support patient education and on-the-job training of nursing staff. In terms of technical support, natural language processing, machine learning models and multimodal interaction constitute the core support. Future research will focus on interdisciplinary integration, intelligent upgrading and global cooperation, but challenges such as data privacy, technical limitations and imperfect evaluation system still need to be addressed.