From leadership research to district action: Evolution of China’s industry–university–research–hospital collaboration in AI medical devices and implications for medical institutions and public health governance

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: research-article. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Against the backdrop of China’s rapid expansion of artificial intelligence (AI), and intelligent healthcare policies, the integration of AI into medical devices is reshaping healthcare delivery and innovation systems. Based on patent data on industry–university–research cooperation in the China AI medical device industry from 2019 to 2025, this study adopts social network analysis and geographical detector methods to systematically examine the spatiotemporal evolution and influencing factors of the innovation network across three dimensions. The findings reveal that at the microagent level, the early dominant pattern of enterprises has been gradually reconstructed by the participation of universities, research institutes, and medical institutions, with medical institutions rising from marginal participants to core intermediaries. At the provincial level, the network exhibits prominent unbalanced agglomeration characteristics. Beijing’s status as a national hub has been continuously strengthened, and a multipolar spatial pattern has taken initial shape. At the level of the cooperation type, intraprovincial cooperation is dominated by industry–industry collaboration, while interprovincial cooperation has formed a cross-regional synergy framework with Beijing as the core, radiating to the Yangtze River Delta and the Pearl River Delta. The value added of the financial industry, the volume of import and export trade, and the transaction volume of the technology market are revealed as the core factors explaining the network status of provinces, whereas the explanatory power of the proportion of education expenditure remains weak. This study contributes to a deeper understanding of China’s AI medical device industry and offers practical insights regarding public health governance.

Study methods

The data used in this paper are derived from the Incopat Global Patent Database. The data retrieval and processing steps are as follows: First, a total of 17,242 patents were retrieved from the database with the knowledge-intensive classification, 502 medical device manufacturing, the emerging industry classification 1.5 artificial intelligence, more than one patent applicant, and China as the publication country. Second, through data screening and sorting, 6,685 relevant patents from 2019 to 2025 were ultimately obtained. Third, for patent applicant cooperation, if there were three applicants (e.g., A, B, and C), it was defined as one cooperation between A and B, one between A and C, and one between B and C. This method was used to construct the IUR collaborative innovation network of patent applicants in the AI medical device industry. Fourth, the Qixin Huiyan Enterprise Database was used to query the provincial locations of patent applicants in batches, from which the IUR collaborative innovation network of provincial regions in the AI medical device industry was constructed. Fifth, according to the IURH classification, stakeholders were categorized as follows: industry includes enterprises and other industrial entities; universities include universities and graduate schools; research institutes include academies and research institutes; and hospitals include general hospitals and affiliated hospitals (26–28). Due to data limitations, this paper does not distinguish between entities engaged in innovation activities and the patent applicants.

Limitations

There are limitations in data coverage. IURH cooperation takes various forms, and patent cooperation is only one of them. Nonpatent forms of cooperation, such as technology contracts, joint laboratories, joint talent training, and the co-publication of papers and monographs, are not included in the analysis, which may affect the presentation of network integrity. In addition, the adopted data are restricted to patents registered in China, failing to capture the impacts of overseas-related factors, such as Sino-foreign cooperative patents, overseas technology licensing, and clinical collaboration, on the quantity and quality of patents. Future research can integrate multi-source and cross-national data to establish a more comprehensive evaluation system for domestic IURH cooperation in China. Also, this study classifies innovation actors primarily according to the organizational identities recorded in patent application data and does not further distinguish the complex overlaps between actual innovation performers and formal patent applicants. Future research can use patent data or information on research projects to further identify the primary entity of innovation.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Feng Hu; Huijie Yang; Zhimin Ren; Xiaoping Wang; Haiyan Zhou; Shaobo Yang; Shaobin Wei; Jiahan Hu; Shuang Zhao; Hao Hu; Junyu Cheng. Evolution of China’s industry–university–research–hospital collaboration in AI medical devices and implications for medical institutions and public health governance. Frontiers in Public Health (2026-06-10). https://doi.org/10.3389/fpubh.2026.1852483

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.