CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
8. Specific Applications of Healthcare AI 8.1 Clinical Decision Support In China, AI-based clinical decision support systems (CDSS) are regulated under a “general regulation + technical guidance” approach. At the general level, they are subject to the Medical Devices Supervision Regulation and the Registration and Filing of Medi - cal Devices Measures, and are typically classified as Class III medical devices when they involve diagnos - tic or therapeutic decision-making. At the technical guidance level, several documents apply to AI-based CDSS, including those powered by AI. For example, the Good Practices for the Application of the Clinical Decision-making Support System for Medical Institu - tions (for Trial Implementation) sets out requirements for ethical review, clinical governance, safety and sys - tem integration within hospitals. In practice, regulatory views and recent pilot cases indicate that developers need to disclose training data sources and validate model performance. For example, the Guiding Principles for AMD Registra - tion Review emphasise that AI-based medical devices (including AI-based CDSS) shall undergo performance verification, including in relation to sensitivity, specific - ity and consistency with clinical standards. Hospitals are also expected to conduct ethical reviews, ensure system traceability and monitor diagnostic perfor - mance. Responsibility is shared among developers, institutions and clinicians. 8.2 Diagnostic Applications AI-based diagnostic tools are regulated under the “general regulation + technical guidance” approach, applying the same core frameworks as AI-based CDSS. They are typically classified as Class II or III medical devices based on their risk profile. To address domain-specific challenges, regulators and industry bodies have issued supplemental technical guide - lines. For example, the Center for Drug Evaluation (CDE) of the NMPA released the Review Guidelines for AI-based Pulmonary Nodule Detection Software via CT Imaging, and the Artificial Intelligence Medical Device Innovation and Cooperation Platform issued the Key Review Points for Deep Learning-Assisted Decision-Making Medical Device Software. These
documents clarify regulatory expectations regarding training data, algorithm validation, clinical applicability and risk mitigation. Under these frameworks, developers are generally required to provide clinical validation data, define algorithm performance metrics (such as sensitivity and specificity), and demonstrate proper data gov - ernance and human oversight mechanisms. 8.3 Therapeutic and Treatment Planning AI systems used in treatment planning are also regu - lated under the “general regulation + technical guid - ance” approach and are typically classified as Class III medical devices if they directly influence therapeu - tic decisions. Additionally, the Regulatory Rules for Internet-based Diagnosis and Treatment (Trial) explic - itly prohibit AI from replacing licensed HCPs in deliv - ering care or issuing prescriptions. In practice, such systems are treated as assistive tools that support but do not substitute for clinical judgment. Currently, there are no dedicated technical guidance documents for treatment-planning AI. Nonetheless, oversight principles follow existing frameworks for clinical decision support: licensed HCPs shall validate AI outputs, and medical institutions remain respon - sible for ethical oversight, system traceability and patient safety. 8.4 Remote Monitoring and Telemedicine AI applications and devices used for remote patient monitoring and telemedicine are subject to specific regulatory requirements, including government fil - ing/registration for medical devices, filing/registra - tion for AIGC products and requirements related to human oversight, data protection, ethical review and user training (for medical devices), as well as medical records with respect to data accuracy, completeness, integrity and traceability, etc. Remote patient monitoring and AI use in home or non-clinical settings may encompass mobile medi - cal devices and general wearables for consumers. In addition to privacy, data quality and security require - ments, clear product handbooks and user training materials are essential to ensure proper use of AI sys - tems, especially for medical devices to be used by
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