Healthcare AI 2025

CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners

3.2 Pre-Market Requirements Pre-market requirements for healthcare AI developers in China mainly apply to AI-based medical devices, which are regulated under the Medical Devices Super - vision Regulation, Registration and Filing of Medical Devices Measure, and relevant technical guidelines. Pursuant to the Guiding Principles for AMD Regis - tration Review and the Guiding Principles for SaMD Registration Review, developers must: • conduct clinical evaluations – unless explicitly exempt – through clinical trials or literature-based analysis; • prepare technical documentation, including algo - rithm descriptions, software life cycle records, data governance protocols, and quality control for train - ing and testing datasets; and • perform risk assessments that demonstrate trace - ability, reliability and safety throughout the product life cycle, along with defined usage limitations. Regulators also require disclosure of algorithm structure, training data and performance metrics. To enhance transparency and interpretability – especially for deep learning models – visual tools such as heat - maps are often encouraged. Furthermore, developers must mitigate bias through representative data col - lection and fairness assessments. For transparency, explainability and bias mitigation, please see 5.2 Transparency and Explainability and 5.3 Bias and Fairness . 3.3 Post-Market Surveillance Post-market surveillance requirements differ based on application types. For hospital-deployed medical AI, according to the Administrative Measures for Adverse Drug Reaction Reporting and Monitoring, which require institutions to report adverse drug reactions, there is currently no overarching legal framework spe - cifically addressing AI-related risks. AI-based medical devices are subject to the Medical Devices Super - vision Regulation, which mandates that registrants and filing holders conduct adverse event monitoring, re-evaluate marketed devices and implement recall mechanisms where necessary.

Concerning algorithm updates, as outlined in 2.4 Soft- ware as a Medical Device (SaMD) , developers must: • verify and validate the safety and effectiveness of any self-learning or updated models; and • apply for change registration when such updates materially affect the product’s intended use or safety profile. For adaptive or continuous learning algorithms, the Guiding Principles for AMD Registration Review require that such features remain disabled or used solely for research purposes unless separately approved. These models, which update based on real-world data, introduce uncertainty in safety and effectiveness. Developers must validate any changes resulting from self-learning and apply for registration modification before such updates can be deployed in clinical settings. A centralised adverse event reporting system exists for medical devices for monitoring and reporting adverse events; however, no dedicated monitoring mechanism is in place for AI applications outside the scope of medical device regulation. 3.4 Enforcement Actions Regarding enforcement, administrative penalties have been imposed on the use of unregistered AI-based medical software and on health data breaches. How - ever, no publicly reported cases of regulatory interven - tion, warnings or product recalls specific to healthcare AI have been identified. Penalties vary by violation type. Under the Medical Devices Supervision Regulation, use of unregistered Class II/III AI medical devices may trigger confiscation, fines or business suspension. Under the DSL, data protection failures may lead to fines of up to CNY2 million, suspension of operations or licence revoca - tion. Although no significant or systematic enforce - ment against healthcare AI has been seen, in June 2023, a Beijing software company developing human gene exome data analysis systems was fined for fail - ing to implement sufficient data security measures, resulting in 19.1 GB of genetic data being exposed to the risk of leakage.

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