ArticleFrontiers in public health2025
Spatial structure and organization of the medical device industry urban network in China: evidence from specialized, refined, distinctive, and innovative firms.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Multi-task deep learning framework combining CNN: vision transformers and PSO for accurate diabetic retinopathy diagnosis and lesion localization.Scientific reports · 2025Article
- Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges.Healthcare (Basel, Switzerland) · 2025Review
- Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.Diagnostics (Basel, Switzerland) · 2025Review
- Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.Scientific reports · 2025Article
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Mamba-fusion for privacy-preserving disease prediction.Scientific reports · 2025Article
- A lightweight scalable hybrid authentication framework for Internet of Medical Things (IoMT) using blockchain hyperledger consortium network with edge computing.Scientific reports · 2025Article
- Spatial networks of China's specialized, refined, distinctive, and innovative medical device firms based on parent-subsidiary contacts: implications for regional health policy.Frontiers in public health · 2025Article
- Innovation networks in the advanced medical equipment industry: supporting regional digital health systems from a local-national perspective.Frontiers in public health · 2025Article
- Study on medical professionals' acceptance of and factors influencing drone delivery for medical supplies.Frontiers in public health · 2025Article
- Spatial dynamics of lithium battery recycling enterprises in China: implications for smart waste management and public health.Frontiers in public health · 2025Article
- NeuroMorphFusion: A Neuro-Inspired Hybrid Learning Framework for Interpretable Deep Lesion Detection in IoT-Enabled Healthcare Systems.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors.
Funding
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Abstract
Introduction: Investigating the network of firms in a specific industry helps explain industrial location and urban functions and provides guidelines for promoting industrial restructuring and high-quality development. Methods: This study develops a network model for the relationship between firms and cities based on the data of listed Specialized, Refined, Distinctive, and Innovative (SRDI) medical device manufacturing firms in China to identify the spatial distribution and influencing factors of the urban network of such firms using network analysis and GeoDetector. Results and disscusion: Three conclusions are obtained from the study. First, the urban network of listed SRDI medical device manufacturing firms exhibits a sparse structure, with the density decreasing from east to west, and the out-degree presenting significant spatial concentration. Suzhou, Shanghai, and Shenzhen are the core of the network power. The in-degree presents low spatial concentration. Clearly differentiated network functions are observed. Second, significant spatial differences are noted between high- and low-level linkage networks from the perspective of corporate governance structure. Third, economic level, labour costs, level of opening-up, talent base, and technological innovation capability have significant effects on the urban network of listed SRDI medical device manufacturing firms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.