CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
• the laws governing medical institutions’ respon - sibility for managing medical records, such as the Regulations for Medical Institutions on Medi - cal Records Management, and the requirement for institutional approval for external data sharing under the Administrative Measures for the Cyber - security of Medical and Healthcare Institutions. No dedicated healthcare AI legislation has been enacted in this regard. As required by the PIPL, medical institutions collabo - rating with enterprises on healthcare AI development must strictly comply with the notification and separate consent requirements before sharing patients’ data. A data sharing agreement must be established to define the scope, purpose and means of data sharing and responsibilities. Cross-border transfer of personal information and important data for healthcare AI development shall also comply with the CBDT mechanisms required by CAC, such as security assessment and stand - ard contractual clause (SCC) filing. If training data involves human genetic resources, the Regulations on the Administration of Human Genetic Resources also require that mandatory filing and data backup be completed before such data can be lawfully trans - ferred outside of China. 6.4 De-Identification and Anonymisation The de-identification and anonymisation of health data are primarily governed by the PIPL – which has established clear definitions for personal information de-identification and anonymisation – and the recom - mended national standard GB/T 37964-2019 Guide - lines for De-identifying Personal Information, which provides detailed guidance on de-identification meth - ods such as aggregation, encryption, suppression, pseudonymisation, generalisation and randomisation. As raw health and medical data still constitute person - al information, many AI system developers are consid - ering the feasibility of de-identifying and anonymising such data for training purposes, to be exempted from the compliance requirements under the PIPL. While there are no legal standards for health and medical data anonymisation as of yet, AI system develop - ers are adopting multiple de-identification measures,
aiming to minimise the risk of re-identification to an acceptable level.
7. Intellectual Property Issues Regarding Healthcare AI 7.1 Patent Protection
Under the Chinese Patent Law, an invention must be a novel technical solution that solves technical prob - lems using natural laws and leads to technical effects. Purely abstract algorithms or mental methods, includ - ing AI models that do not have practical applications or technical implementability, are not patentable. To form a complete “problem–means–effect” chain, the healthcare AI patent application must clearly show how the technical systems solve specific techni - cal issues (eg, how the algorithm is embedded in image-capturing devices or diagnostic apparatus). In practice, the Guidelines for Patent Applications for AI-related Inventions, published by the National Intel - lectual Property Administration (CNIPA), further clarify that, due to the “black-box” nature of AI, the patent specifications must include experimental data and parameter relationships so as to meet the implemen - tation requirements. Further, Article 25.1.3 of the Patent Law prohibits patents for diagnosis and treatment methods for ill - nesses. This limitation poses a significant barrier to healthcare AI patent applications that directly involve medical diagnoses. In patent examination practice, AI algorithms that directly diagnose diseases from patient data are typically considered “diagnostic methods” and are excluded from patent protection. To navigate this restriction, companies often reframe their inven - tions to avoid the word “diagnosis” and emphasise systems and devices rather than diagnostic methods. A notable case illustrating the successful application of healthcare AI in China is Tencent’s MiYing AI for glaucoma diagnosis, which passed the regulatory requirements for medical devices and was approved as an innovative medical instrument. This case dem - onstrates that AI applications integrated with medical equipment and following specific technical and regu - latory guidelines can be successfully patented.
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