Clinical Ethical Governance of Generative Medical Artificial Intelligence (GMAI): A Three-Dimensional Collaborative Pathway and Chinese Practices

Corresponding author: Dai hui, 13889903995@139.com

DOI: 10.12201/bmr.202511.00057

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  Abstract: Purpose/SignificanceThis study aims to address core ethical challenges such as algorithmic black boxes and ambiguous liability in the clinical application of Generative Medical Artificial Intelligence (GMAI), construct an ethical governance system adapted to China’s healthcare context, and provide theoretical support and practical references for balancing GMAI technological innovation with ethical constraints to promote the high-quality development of medical AI.Methods/ProcessBased on the characteristics of China’s healthcare system, data ecosystem, and cultural context, this paper compares differences in GMAI governance scenarios between China and other countries. A three-dimensional collaborative governance framework (technology, institution, and society) is established, with a detailed analysis of the responsibilities of multiple stakeholders including patients, physicians, and technology platforms. Key practical pathways such as federated learning, hierarchical supervision, and ethical sandboxes are elaborated.Results/ConclusionsA three-dimensional collaborative governance scheme for GMAI clinical ethics is proposed: technological security guarantee - dynamic institutional regulation - multi-stakeholder social governance. At the technical level, federated learning enables secure data sharing; at the institutional level, a hierarchical supervision mechanism of strict control for high-risk applications and flexible management for low-risk ones is established; at the social level, ethical sandboxes and multi-stakeholder governance models are integrated. This scheme effectively responds to the specific needs of GMAI governance in the Chinese context and provides replicable Chinese practices for global medical AI ethical governance. Future efforts should focus on bridging the digital divide at the grassroots level and optimizing the judicial connection of dynamic liability mechanisms.

  Key words: AI medical ethics; federal learning; gradient penetration governance; dynamic responsibility mechanism; Chinese practice

  Submit time: 20 November 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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