CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
while safeguarding data rights and security. Shanghai and Shenzhen are piloting similar approaches. Beijing’s Data Foundation System Pilot Zone and AI Data Training Base together provide trusted infrastruc - ture for developing innovative AI data mechanisms. By integrating computing, data and compliance solutions, they offer comprehensive support to LLM developers. This represents China’s first successful implementa - tion of an AI regulatory sandbox model, which may gradually extend nationwide. Beijing’s AI + Health - care Action Plan (2025–27) further proposes a com - prehensive support framework to boost healthcare AI development, including fast-track review channels for innovative AI medical devices, prioritised approvals, enhanced policy and financial incentives. By 2027, these measures aim to establish an innovative, global - ly influential healthcare ecosystem covering the entire China actively engages in international efforts to har - monise healthcare AI regulation, participating in bod - ies like the International Medical Device Regulators Forum (IMDRF), World Health Organization (WHO) and International Organization for Standardization (ISO). China contributes to global rulemaking on AI safety, transparency and data governance, and shares agile regulatory approaches through platforms like the Belt and Road Digital Cooperation Network. WHO and IMDRF guidelines have influenced China’s focus on life cycle management, clinical validation and algo - rithm transparency. ISO standards also inform national and industry-level AI quality and data governance frameworks. Cross-border challenges remain for healthcare AI developers; please refer to 10.5 Cross-Border Con- siderations for more details. 9.4 Emerging Legal Challenges Key challenges include assigning liability for auto - mated AI decision-making, clarifying the fair use of de-identified or copyrighted training data, and ensur - ing algorithm transparency and fairness – especially in critical medical scenarios. Data quality gaps (eg, insufficient data volume for rare diseases, and inade - value chain from R&D to application. 9.3 International Harmonisation
quate data diversity and representativeness) and poor generalisability further complicate oversight. Regulators are responding by (i) drafting laws and regulations (see 9.1 Pending Legislation and Regu- lation ); and (ii) exploring dynamic supervision for con - tinuously learning systems, requiring regular perfor - mance reports and stricter data governance. Concerning autonomous AI, future laws may define its legal status and clarify responsibilities among devel - opers, users and institutions. Integration with robotics or virtual reality (VR) also gives rise to cross-sector co-ordination needs. Healthcare AI developers need to implement “compli - ance by design” from the outset, embedding regula - tory considerations into every stage, from data sourc - ing to algorithm explainability, establishing dynamic oversight through regular algorithm evaluations and maintaining detailed documentation of training data, validation reports and decision paths, forming a com - prehensive AI model life cycle document. As general practice in AI governance, the following measures could be taken into consideration: • establishment of multidisciplinary AI ethics com - mittees within organisations, comprising HCPs, legal experts and IT experts; • documentation of the entire AI life cycle, with clear version control and audit trails; • Monitoring systems tracking technical perfor - mance, data usage and clinical outcomes; and • compliance that addresses cross-border data flow, cybersecurity and algorithm validation per China’s evolving legal landscape. As outlined in 9.2 Regulatory Sandboxes and Innova- tion Programs , regulatory sandboxes can facilitate a more effective balance between fostering innovation and ensuring compliance. 10. Practical Considerations in Healthcare AI 10.1 Compliance Strategies
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