Chinese Journal of Medical Education ›› 2025, Vol. 45 ›› Issue (7): 496-501.DOI: 10.3760/cma.j.cn115259-20240730-00800

• Educational Technologies • Previous Articles     Next Articles

Exploring collaborative governance pathways for artificial intelligence empowered medical education

Zhang Ruyi1, Zhou Yun′ao1, Peng Yingchun2   

  1. 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:2024-07-30 Online:2025-07-01 Published:2025-07-01
  • Contact: Peng Yingchun, Email: pycjql@ccmu.edu.cn
  • 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)

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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