Data Risks, Compliance Obligations, and Countermeasures for Medical Corpora in Generative AI

Corresponding author: fang an, fang.an@imicams.ac.cn

DOI: 10.12201/bmr.202512.00083

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  Abstract: Purpose/Significance This study investigates the data risks associated with medical corpora in generative artificial intelligence (GenAI). It aims to explore the compliance obligations and risk mitigation strategies relevant to such corpora in China, thereby contributing to the development of compliant governance methods for medical datasets for GenAI. Method/Process Based on risk and compliance management principles and theory, the study examined the data lifecycle of corpora in GenAI and reviewed China’s legal and regulatory framework concerning GenAI and medical data. It then proposed targeted solutions to address three major risks: data security and privacy, training data bias, and data legitimacy. Result/Conclusion The research focuses on the pressing compliance issues in medical corpora in GenAI and presents three practical approaches. First, it emphasizes legality as the foundation, following key principles such as purpose limitation, data minimization, data rights protection, and risk prevention. Second, it aligns data collection and annotation practices with laws in China, national standards, and industry guidelines. Third, it leverages automated tools to help identify and manage risks throughout the data lifecycle. These findings offer insights for improving the compliant development and application of healthcare contexts for GenAI.

  Key words: generative artificial intelligence; medical corpora; data compliance; compliance obligations; risk mitigation

  Submit time: 31 December 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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