Ethical Risk Identification, Governance, and Foresight of Medical Foundation Models

Abstract

The medical field is undergoing an intelligent transformation characterized by the emergence of multimodal foundation models. While technological empowerment brings unprecedented opportunities, it is accompanied by profound ethical risks. The deep application of medical foundation models has amplified such risks, necessitating comprehensive identification and effective governance strategies. This study systematically examines four core categories of ethical risks in medical foundation model applications: data privacy risks, algorithmic decision-making risks, subject-relation risks, and social equity risks, illustrated through representative cases. To address these challenges, a data‒algorithm‒application‒law integrated governance framework is proposed, encompassing the establishment of robust data governance systems, innovation of algorithmic governance mechanisms, formulation of clinical application standards, and improvement in legal and regulatory frameworks. Furthermore, the study analyzes key technological and policy challenges constraining the development of medical foundation models. Looking ahead, the study outlines potential future directions, including the exploration of blockchain-based medical data ownership confirmation, development of lightweight models to promote equitable healthcare at the grassroots level, and construction of collaborative ecosystems integrating government, industry, academia, research, and healthcare. These efforts are intended to provide theoretical foundations and practical pathways for fostering the normative and sound development of medical foundation model technologies, ensuring patient rights, and enhancing the ethical governance system in the healthcare domain.

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