随着扩展现实、人工智能、数字人、虚拟患者、数字孪生及医疗物联网等技术快速发展，医学教育正由以课堂讲授和机会性床旁学习为主的传统模式，逐步转向以情境化、交互化、连续化和胜任力导向为特征的新模式。元宇宙医学并非单一设备或单一软件，而是以虚实融合、实时互联、沉浸交互和数据驱动为核心特征的医学应用生态。呼吸专科教学兼具知识体系复杂、动态监测要求高、程序性技能密集和团队协作依赖强等特点，因此成为元宇宙医学较具应用潜力的专科领域。现有研究表明，XR/VR/AR、虚拟患者、仿真教育及大语言模型辅助教学有助于改善知识掌握、操作技能、学习投入和病例推理能力，但其长期迁移效应、真实临床结局和成本效益证据仍需进一步积累。在呼吸专科中，数字人教师、虚拟病例、虚拟病房、支气管镜 与 EBUS 仿真训练、呼吸衰竭识别及机械通气沉浸式教学，是当前最具现实意义的重点场景。白春学教授团队提出的元宇宙医学、医学 GPT与 BAIMGPT 等概念，为我国呼吸教学智能化升级提供了本土化理论支点和实践路径。本文围绕元宇宙医学在呼吸专科教学中的理论基础、关键应用场景、教学价值、现实挑战及实施路径展开综述，以期为呼吸医学教学改革和人才培养体系优化提供参考。

Medical education is moving beyond a model dominated by classroom teaching and opportunistic bedside exposure toward one characterized by contextualization, interactivity, continuity, and competency-based learning, driven by the rapid development of extended reality, artificial intelligence, digital humans, virtual patients, digital twins, and the Internet of Medical Things. Metaverse medicine should not be understood as a single device or software platform; rather, it represents an integrated educational and clinical ecosystem built on immersive interaction, virtual-real fusion, real-time connectivity, and data-driven learning. Respiratory education is particularly well suited to this transformation because it involves complex knowledge structures, dynamic monitoring, procedure-intensive training, and strong reliance on teamwork and workflow coordination. Current evidence suggests that XR/VR/AR, virtual patients, simulation-based education, and large language model-assisted teaching can improve knowledge acquisition, procedural performance, learner engagement, and clinical reasoning, although stronger evidence is still needed regarding long-term transfer, real-world clinical outcomes, and cost-effectiveness. In respiratory education, digital human teachers, virtual cases, virtual wards, bronchoscopy and EBUS simulation, immersive training for respiratory failure recognition, and mechanical ventilation education appear to be the most practice-relevant and immediately actionable scenarios. In parallel, concepts proposed by Professor Chunxue Bai’s team—including metaverse medicine, medical GPT, and BAIMGPT—provide an important localized theoretical foundation and implementation pathway for the intelligent upgrading of respiratory education in China. This review summarizes the theoretical basis, major application scenarios, educational value, practical challenges, and implementation strategies of metaverse medicine in respiratory education, with the aim of informing future educational reform and talent development in respiratory medicine.