中华医学教育杂志

中华医学教育杂志 >

2025 , Vol. 45 >Issue 7: 496 - 501

DOI: https://doi.org/10.3760/cma.j.cn115259-20240730-00800

教育技术

人工智能赋能医学教育的协同治理路径探析

  • 张如意 ,
  • 周运翱 ,
  • 彭迎春
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  • 1首都医科大学附属北京地坛医院伦理委员会办公室,北京 100015;
    2首都医科大学医学人文学院医学伦理学与医学史学学系,北京 100069

收稿日期: 2024-07-30

  网络出版日期: 2025-07-01

基金资助

首都医科大学医院管理研究所2023年开放性课题(2023YGS09);首都医科大学附属北京地坛医院2024年院内科研基金(DTZK-202403);首都医科大学2024年教育教学改革研究课题(2024JYY059)

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Exploring collaborative governance pathways for artificial intelligence empowered medical education

  • Zhang Ruyi ,
  • Zhou Yun′ao ,
  • Peng Yingchun
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  • 1Ethics Committee Office, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China;
    2Department of Medical Ethics and History of Medicine, School of Medical Humanities, Capital Medical University, Beijing 100069, China

Received date: 2024-07-30

  Online published: 2025-07-01

Supported by

Open Project of Hospital Management Institute of Capital Medical University in 2023(2023YGS09); The Science Fundation of Beijing Ditan Hospital of Capital Medical University in 2024(DTZK-202403); Education and Teaching Reform Research Project of Capital Medical University in 2024 (2024JYY059)

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摘要

人工智能(artificial intelligence,AI)在数据挖掘、图像处理、机器学习等方面的强大能力正在推动着医学教育的变革,其在赋能教学创新的同时,也面临着可解释性不足、内容准确性存疑、数据安全风险、师生认知偏差和监管机制缺失等挑战。为了AI赋能医学教育的可持续发展,本研究采用文献分析与理论研究相结合的方法,基于利益相关者理论和协同治理理论,系统分析AI赋能医学教育的核心挑战和工程师、医学专家/医学教育工作者、学生和教育机构等利益相关者的协同治理路径。AI赋能医学教育需要多元主体协同全周期治理:生产环节加强数据治理,提升AI可解释性与准确性;质控环节建立标准评估体系;准入环节严格审核并采取权益保护措施;使用环节提升师生AI素养;监管环节动态跟踪风险;问责环节明晰责任边界。通过技术创新、制度完善和主体能力建设等多方面的有机结合,实现医学教育与现代科技的融合发展。

关键词： 人工智能; 医学教育; 利益相关者; 协同治理

本文引用格式

张如意 , 周运翱 , 彭迎春 . 人工智能赋能医学教育的协同治理路径探析[J]. 中华医学教育杂志, 2025 , 45(7) : 496 -501 . DOI: 10.3760/cma.j.cn115259-20240730-00800

Abstract

The transformative potential of artificial intelligence (AI) in medical education, characterized by its robust capabilities in data mining, image processing, and machine learning, is widely recognized. However, several challenges have been identified, including limited interpretability, questionable content accuracy, data security risks, cognitive biases among educators and students, and the absence of regulatory mechanisms. To address these concerns and ensure the sustainable development of AI-empowered medical education, a comprehensive study was conducted employing literature analysis and theoretical research.Based on stakeholder theory and collaborative governance theory, it systematically analyzes the core challenges of AI in medical education and explores the collaborative governance pathways among stakeholders such as engineers, medical experts/medical educators, students, and educational institutions. Effective governance was found to necessitate multi-stakeholder engagement across all implementation phases. During production, data governance must be strengthened to enhance AI interpretability and accuracy. For quality assurance, standardized evaluation systems should be established. Prior to deployment, strict reviews and rights protection measures are essential. In application, improving AI literacy among educators and students is critical. Ongoing regulation necessitates dynamic risk monitoring, while accountability mechanisms must clarify responsibility boundaries. Through the integration of technological innovation, institutional refinement, and stakeholder capacity-building, the convergence of medical education and modern technology can be achieved.

Key words： Artificial intelligence; Medical education; Stakeholders; Collaborative governance

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