CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
7.2 Copyright and Trade Secrets Copyright Protection
and external experts are prohibited from disclosing technical information or other trade secrets obtained during the regulatory process without the applicant’s consent. To mitigate the risk of repeated disclosure, a “master file” system has been introduced, enabling companies to file core algorithmic materials separate - ly and authorise their being referenced across multiple product applications. Meanwhile, the Guiding Principles for AMD Registra - tion Review mandate transparency by requiring com - panies to disclose key information – such as algorithm performance, data provenance and training processes – to ensure product safety. For clinical decision sup - port tools, product manuals shall include performance evaluations and a summary of training data. For black- box models, additional disclosures regarding usage limitations and risk warnings are required. In practice, companies typically meet these transparency require - ments through summary disclosures and performance reports while safeguarding detailed algorithms as Health AI outputs (eg, diagnostic findings, treatment suggestions) are often deemed part of medical ser - vices and are generally not recognised as indepen - dently tradable IP. • Diagnostic recommendations, treatment recom - mendations and other outputs generated by healthcare AI systems are generally regarded as analytical outcomes rather than original expres - sions and thus are typically not independently protectable by copyright or patent. • Healthcare AI outputs are merely the result of using a (potentially patented) tool; the outputs them - selves are not novel “technical solutions”, thus failing to meet the patentability criteria. • For copyright protection, current law lacks specific provisions regarding AI outputs. As elaborated in 7.2 Copyright and Trade Secrets , Chinese courts internal confidential information. 7.3 Ownership of AI Outputs have recognised that when the AI outputs cre - ated by natural persons reflect original expression, such outputs may obtain copyright protection. In practice, however, healthcare AI outputs normally lack human authorship and sufficient originality of form, and generally do not qualify as “works” under
In China, Article 3 (8) of the Copyright Law expressly lists computer software among the categories of cop - yrightable works. This statutory protection is further elaborated in Articles 2 and 3 of the Regulations on the Protection of Computer Software, which specify that software, including computer programmes, source code and accompanying documentation qualify for copyright protection once the originality requirement has been met. Copyright protection is automatically granted upon creation without the compulsory need for registration, although voluntary registration is com - monly used for evidentiary purposes in practice. In the context of healthcare AI, the underlying algorith - mic logic and structure of training models generally do not meet the threshold for authorship under copyright law and thus lack direct copyright protection. As not - ed in the foregoing, patent protection for AI algorithms integrated into concrete technical solutions remains uncertain. As a result, healthcare AI companies tend to rely more on trade secret protection to safeguard core models, parameters and data preprocessing workflows. Trade Secret Protection Pursuant to Article 9 (4) of the Anti-Unfair Competi - tion Law, technical information may qualify as a trade secret if it is not publicly known, commercially valu - able and subject to reasonable confidentiality meas - ures. In practice, healthcare AI companies treat key elements such as model weights, training datasets, algorithm design frameworks and operational pro - cesses as trade secrets. Protection mechanisms typically include non-disclosure agreements, infor - mation compartmentalisation, encrypted storage and access controls. Companies also implement clear internal policies on employee IP ownership and post- employment non-compete obligations to mitigate the risk of misappropriation or disputes over employee inventions. Regulatory Disclosure and Confidentiality Mechanisms In the context of medical device registration, health - care AI developers shall submit detailed technical documentation to regulatory authorities. Reviewers
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