精准医学教育持续推进背景下，教学医院医学教育治理已由传统经验型管理逐步走向数据支持型管理，但现有模式整体上仍主要停留于教育证据链形成、画像分析和风险预测阶段，对干预后果推演、治理方案比较及系统级优化的支撑相对不足。基于此提出“医学教育治理数字孪生（medical education governance digital twin，MEGDT）”概念，并通过结合数字孪生相关理论、教学医院数据治理实践及国际前沿案例，讨论 MEGDT 的概念内涵、实现构想及其治理价值。MEGDT 的潜在价值不仅在于提升医学教育治理的动态感知、推演和反馈能力，还在于为教学医院在培养效果、资源投入、组织效率与教育公平之间寻求更优平衡提供决策支持。当前，MEGDT 仍处于概念提出与路径探索阶段，后续宜围绕毕业后医学教育等场景开展最小可行原型研究，并同步关注数据质量、模型可信度、隐私安全及成本效果平衡等问题。

In the context of the ongoing advancement of precision medical education, governance in medical education within teaching hospitals has gradually shifted from traditional experience-based management to data-supported management. However, current approaches still largely remain at the stages of educational evidence-chain construction, profiling analysis, and risk prediction, with relatively limited capacity to support intervention consequence simulation, governance strategy comparison, and system-level optimization. Against this background, the concept of the medical education governance digital twin (MEGDT) is proposed. By integrating digital twin theory, data governance practices in teaching hospitals, and international frontier cases, this paper discusses the conceptual connotation, implementation framework, and governance value of MEGDT. The potential value of MEGDT lies not only in enhancing the dynamic perception, simulation, and feedback capabilities of medical education governance, but also in providing decision support for teaching hospitals to achieve a better balance among educational effectiveness, resource input, organizational efficiency, and educational equity. At present, MEGDT remains at the stage of conceptual proposal and pathway exploration. Future work should prioritize minimum viable prototype studies in scenarios such as postgraduate medical education, while also addressing data quality, model credibility, privacy and security, and cost-effectiveness balance.