CHINA Trends and Developments Contributed by: Hanshuo Zhou, Xiaoyun Wang and Taige Shi (Casper), Jingtian & Gongcheng
Emerging AI-driven business models in the digital health industry AI+ medical imaging As the sector continues to evolve rapidly, devel- opers and operators of AI imaging solutions are encountering a host of regulatory challenges that span the entire product life cycle – from data acquisition and algorithm training to registra- tion and post-market responsibilities. One of the earliest hurdles arises during model devel- opment: the legal basis for training datasets. Companies must ensure that patient data used to train diagnostic algorithms has been lawfully sourced, with appropriate informed consent and adequate anonymisation, in line with data pro- tection and ethical standards. Beyond data governance, classification uncer- tainty presents a critical compliance issue. AI imaging software must be evaluated to deter- mine whether it qualifies as a standalone Soft- ware as a Medical Device (SaMD) or as embed- ded software (SiMD) – a distinction that directly affects the applicable registration pathway with the National Medical Products Administration (NMPA). In parallel, the software’s risk class – whether Class II or Class III – will shape its regulatory burden, including the extent of clinical validation required. Overlaying these concerns is the unresolved question of liability. In the event of adverse events (AEs), serious adverse events (SAEs), or diagnostic errors, it remains unclear how responsibility will be distributed across soft- ware developers, hardware manufacturers, and healthcare providers. This regulatory ambiguity underscores the urgent need for integrated com- pliance frameworks – ones that span technical, legal, and operational dimensions – particularly as regulators intensify scrutiny of AI-enabled clinical tools.
China’s Digital Healthcare: Structuring Compliance for AI-Enabled Clinical Infrastructure Regulatory landscape of China’s digital health sector By 2024, China’s digital health sector had reached an estimated value of USD81.3 billion, marking a period of rapid expansion and grow- ing institutional engagement. In November 2024, the National Health Commission introduced the Reference Guidelines for Application Scenarios of Artificial Intelligence in the Health and Medi- cal Sector – a landmark policy initiative that out- lines four key areas for AI integration: Healthcare Service Administration, Primary Care, Health Industry Innovation, and Medical Education and Research. The guidelines set out 13 representa- tive use cases across these domains, reflecting a clear regulatory intention to promote responsible and prospective adoption of AI in the healthcare system. In parallel, the broad deployment of AI tech- nologies in clinical and operational contexts has significantly raised regulatory expectations around data governance. Compliance obliga- tions related to the collection, storage, process- ing, and transfer of medical data have become increasingly complex. Cross-border data flows, in particular, now implicate overlapping legal concerns, including national security, patient pri- vacy, and co-ordination with international regu- latory standards. As China continues to build out its legal framework and strengthen enforcement capacity, digital health companies are under growing pressure to establish comprehensive internal compliance systems – both to mitigate legal risk and to support the sustainable growth of the sector.
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