From leadership research to district action: Spatiotemporal evolution of innovation collaboration networks in China’s AI medical device industry: implications for 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:

Artificial intelligence (AI) medical devices have become an important technological foundation for enhancing healthcare system resilience and promoting the equitable allocation of medical innovation resources. However, limited attention has been paid to the distribution of upstream innovation resources and the spatial organization of this industry in the wake of the pandemic. Using AI medical device patent collaboration data from the Yangtze River Delta during 2018–2025, this study constructs local and external collaborative innovation networks and applies social network analysis and the Geodetector to examine their spatial evolution and associated factors across different phases of the COVID-19 pandemic. The results show that all innovation networks expanded continuously, particularly the external collaborative network. Nevertheless, cross-regional collaboration remained concentrated in a limited number of core cities, resulting in a persistent core–periphery structure. Regional GDP, retail market size, higher education resources, science expenditure, and financial support consistently exhibited strong explanatory power for collaboration intensity, while GDP growth and openness became increasingly associated with network differentiation following the COVID-19 shock. Moreover, the explanatory power of most variables was stronger for external collaboration than for local collaboration, suggesting that cross-regional knowledge exchange relies more heavily on comprehensive innovation capacity. This study advances the understanding of the spatial organization of AI medical innovation and provides empirical evidence for promoting a more balanced allocation of cross-regional medical R&D resources.

Study methods

Using Gephi software, an analysis of network density, average degree, average path length, and weighted degree centrality was conducted for the local, external collaborative, and two-layer networks of the AI medical device industry in the YRD (64–66). In this paper, Internal Collaboration Innovation Intensity refers to the weighted degree centrality of YRD cities within the Local Network, whereas External Collaborative Innovation Intensity refers to their weighted degree centrality within the external collaborative network.

Limitations

(1) Due to limitations in data availability, this study uses the patent collaboration network as a proxy for regional public health innovation capacity and has not yet incorporated other measures of innovation linkages, such as technology transfer, talent mobility, and capital investment, or other indicators of public health governance capacity. Therefore, the characterization of innovation networks and public health innovation capacity remains somewhat limited. Future research could integrate multi-source data, including patents, academic papers, talent, and capital, to construct a more comprehensive framework for analyzing innovation networks.

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; Junyu Cheng; Zhijie Huang; Shaobo Yang; Xiaoping Wang; Haiyan Zhou; Jiahan Hu; Hao Hu; Shuang Zhao. Spatiotemporal evolution of innovation collaboration networks in China’s AI medical device industry: implications for public health governance. Frontiers in Public Health (2026-08-19). https://doi.org/10.3389/fpubh.2026.1899325

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.