CHINA Law and Practice Contributed by: Gil Zhang, Diana Li, Muran Sun and Yongqi Tao, Fangda Partners
the Copyright Law, thus falling outside copyright protection. Given the premise that the outputs themselves gen - erally do not involve IP rights, contractual practice is unlikely to specifically allocate such rights. Instead, contracts would primarily treat the outputs as data and assign rights and obligations from the perspective of data usage. Due to the absence of specific legal provisions, con - tractual agreements between AI technology provid - ers and healthcare institutions play a decisive role in allocating IP rights and responsibilities. Typically, AI providers retain IP in core technologies, such as algorithms, software and models, while healthcare institutions (eg, hospitals) receive licences to use and deploy the AI outputs as end users. These contracts often address IP as follows: • copyrights of AI software and algorithms belong to the provider, and healthcare institutions may not infringe on providers’ technical secrets through means such as reverse engineering and redistribu - tion; • healthcare institutions typically assume legal responsibility for the final diagnosis and decision- making, regardless of AI participation; and • healthcare institutions may be required to maintain medical liability insurance to cover potential AI- related errors or adverse outcomes. 7.4 Licensing and Commercialisation Commercialisation Models for Healthcare AI A variety of commercialisation models are employed in the healthcare AI sector, including technology licens - ing and collaboration, software-as-a-service (SaaS) subscriptions and direct sales of regulated medical devices. • Healthcare AI companies often licence their technologies to major pharmaceutical or medical device firms to facilitate AI adoption in areas such as drug discovery, imaging and diagnostics involv - ing technical licensing during target identification or early-stage development. Additionally, companies frequently engage in joint innovation projects with
hospitals or pharmaceutical companies to facilitate clinical integration of AI applications. • AI diagnostic services are also offered via cloud platforms, with hospitals subscribing annually or on a per-use basis. This model enables rapid updates and lowers maintenance costs, but raises issues around cybersecurity, network reliability and reim - bursement eligibility. • Healthcare AI companies may also obtain Class II or Class III medical device approvals to directly commercialise their AI-assisted diagnostic or therapeutic products to healthcare institutions. Regulatory and Reimbursement Challenges Under the current Classified Catalogue of Medical Devices, AI diagnostic software offering only clini - cal support is regulated as Class II, while software generating autonomous diagnostic outputs requires Class III approval, including additional clinical trials. The longer approval timeline for Class III products often leads companies to frame their tools as assis - tive. Even after regulatory approval, inclusion in hos - pital billing systems and insurance coverage remains essential for commercial-scale use, yet no AI health - care product is currently reimbursed under China’s public healthcare system. Consequently, commerciali - sation still requires active engagement with healthcare authorities to explore viable reimbursement models. Academic-Industry Collaboration To accelerate clinical adoption, many AI companies collaborate with hospitals and universities by forming joint labs or R&D alliances. These partnerships inte - grate clinical expertise and large-scale medical data, enabling the co-development of AI tools tailored to real-world settings. Notable examples include joint laboratories established by SenseTime and West Chi - na Hospital of Sichuan University, and by iFLYTEK and Anhui Provincial Hospital early in 2016. More recently, Baidu formed an AI hospital consortium with Shen - zhen South Hospital and other partners to explore multi-agent collaborative AI solutions. These collabo - rations have produced widely adopted imaging, tri - age and diagnostics applications, forming replicable models for broader industry advancement.
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