CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
Change Management Effective integration of AI in healthcare requires adapt - ing clinical workflows and ensuring HCP buy-in. AI vendors could support this through: • workflow mapping to align AI with clinical practice; • pilot testing to gather feedback and refine usability; and • ongoing monitoring to ensure safety, compliance and performance. 10.5 Cross-Border Considerations Deploying healthcare AI across jurisdictions presents complex legal and regulatory challenges. Key issues include the diverse requirements for data privacy and protection, medical device governance (such as the different standards for healthcare AI systems that qualify as medical devices versus algorithm-related issues) and AI regulatory frameworks, etc.
To navigate the different regulatory requirements, it is advisable to: • implement data localisation and modular deploy - ment – verify CBDT limits for each major jurisdic - tion, store sensitive data on local servers and design modular AI architectures that process data locally; • create a global compliance framework with local nuances – develop a unified internal standard for AI ethics, quality and compliance, while mapping and adapting to local legal differences (eg, bench - mark General Data Protection Regulation (GDPR) for data privacy and protection issues, and then include jurisdiction-specific compliance add-ons); • use tech-enhanced compliance measures – lev - erage technologies like differential privacy and federated learning to protect data while enabling cross-border AI scalability and minimising reliance on centralised datasets; and • design flexible contracts and liability frameworks – draft jurisdiction-specific agreements with local partners to clearly define responsibilities, data con - trol, algorithm update protocols and audit rights.
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