CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
10.2 Contracting and Liability Allocation Healthcare AI contracts typically address the follow - ing key areas: • IP – core algorithms are usually retained by tech - nology providers, and customised models or out - puts may be co-owned; • regulatory compliance – providers must ensure their products meet applicable medical device and AI-specific regulations; • data and privacy – contracts define data sources, anonymisation standards and compliance with the PIPL and DSL; • liability allocation – AI is used as a clinical support tool, and liability for medical decisions remains with HCPs; and • indemnity and warranties – liability caps and exclu - sions are common, and warranties may cover performance, updates and technical support. 10.3 Insurance Considerations Healthcare AI developers should prioritise insurance coverage that protects them from risks associated with algorithm performance and data processing. One of the most critical types of insurance is errors and omissions insurance, which provides protection in case an AI system malfunctions, delivers incor - rect outputs or fails to perform as expected. If their AI product is classified as a medical device, devel - opers should also secure product liability insurance. Healthcare users should evaluate whether their exist - ing medical malpractice insurance or professional liability coverage extends to the use of AI-assisted tools. In addition, organisations adopting healthcare AI must consider cyber liability coverage to protect cybersecurity and patient data security. Currently, there is no mandatory requirement nor dominant market practice for healthcare AI insurance in China. To address the market gaps, the People’s Insurance Company (Group) of China (PICC) has intro - duced “Affirmative AI Cover” insurance. This liability insurance primarily provides exclusive protection against infringement risks from content generated by LLMs, including copyright, portrait and reputational infringements.
The risk assessment varies significantly between traditional insurers and those offering affirmative AI coverage: • traditional insurers tend to assess AI-related risks in healthcare by relying on established actuarial models and regulatory benchmarks – their focus is on how AI affects clinical workflows and liability exposure, rather than the AI’s technical design; and • insurers providing Affirmative AI Cover take a more technical and adaptive approach – they conduct risk assessments and measurements based on AI application scenarios in the healthcare industry, upstream and downstream business chains, actual model performance, training data sources, etc, to adjust the coverage scope and premium. 10.4 Best Practices for Implementation In China, medical institutions are required to follow the best practices in the Management Specifications for Artificial Intelligence-Assisted Diagnosis Technol - ogy (Trial) and Management Specifications for Artificial Intelligence-Assisted Treatment Technology (Trial) for implementing healthcare AI systems that qualify as medical devices. Organisation and Governance Structure Healthcare organisations should involve ethics com - mittees in the AI system deployment process; a com - mittee should review clinical applicability, patient safe - ty and data usage compliance. Clinical departments and IT teams should co-ordinate implementation, ensuring that systems align with medical workflows HCPs shall meet the requirements outlined in the Management Specification for Artificial Intelligence- Assisted Diagnosis Technology (Trial) and Manage - ment Specifications for Artificial Intelligence-Assist - ed Treatment Technology (Trial), including at least six months of structured training at a certified provincial base, 20+ hours of theoretical study and supervised involvement in over 20 AI-assisted diagnosis cases. Post-training assessment should be conducted to ensure clinical competence in AI system use. and institutional values. Training Requirements
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