ArticleFrontiers in public health2025
Quantitative evaluation and optimization of AI policy and regulatory texts for smart healthcare.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- From Algorithm to Policy: A Bibliometric Analysis of Implementation Science and Governance Frameworks in AI Healthcare Research (2016-2026).Healthcare (Basel, Switzerland) · 2026Review
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Quantitative evaluation of China's smart aging healthcare policy under the background of silver economy development: based on PMC model.Frontiers in public health · 2026Article
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Authors and funding
5 authors.
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Abstract
Background: Artificial intelligence has revolutionized the field of smart healthcare, demonstrating significant value in enhancing diagnostic and treatment efficiency and controlling medical costs. AI in smart healthcare system policies are of great significance for optimizing the allocation of medical resources, promoting the accuracy and efficiency of diagnosis and treatment services. Evaluation of AI in smart healthcare system policy texts can provide theoretical support and decision-making basis for the scientific formulation, effective implementation, adjustment and optimization of AI in smart healthcare system policies. Methods: The study analyzes 10 representative policy texts from 77 policies during 2015-2025, and the strengths and weaknesses of each policy and the optimization and adjustment paths are analyzed by calculating the PMC index and drawing PMC surface and radar diagrams. Results: The findings reveal that the overall quality of AI policies for smart healthcare reaches an "excellent" level, with notable strengths in policy focus and the completeness of evaluation systems. However, challenges persist, including insufficient policy continuity, overreliance on mandatory directives as policy tools, and weak operability of policy measures. Conclusion: The study utilizes the PMC policy standardization assessment to identify policy issues and provides differentiated design references based on regional differences, offering crucial support for the collaborative improvement, scientific construction, and global AI governance optimization of the international AI policy framework.
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