Evaluation of global national-level medical artificial intelligence policies and strategies based on the policy modeling consistency index model

The rapid advancement of artificial intelligence (AI) technologies has profoundly transformed the healthcare sector. Governments in major economies have incorporated medical AI into national strategies or policies, issuing policies to guide innovation, regulation, and ethical use. This study aims to quantitatively evaluate the quality of medical AI policies across major countries and regions using the Policy Modeling Consistency (PMC) Index model, providing empirical evidence to inform the optimization of national level medical AI policy framework. Based on the PMC Index model, a multidimensional evaluation system comprising nine primary and thirty-five secondary variables was constructed to assess the internal consistency, structural completeness, and implementation strength of medical AI policies. Forty-two representative policy documents released between 2019 and 2025 from the United States, the United Kingdom, Japan, South Korea, Australia, China, and the European Union were analyzed through text mining and quantitative modeling. The PMC index and concavity index were calculated to classify policy quality into five grades (A–E) and visualize their multidimensional strengths and weaknesses using PMC surface mapping. Among the 42 policies analyzed, 2 (4.76%) were rated Grade A, 29 (69.05%) were Grade B, 9(14.51%) were Grade C, and 2 (4.76%) Grade D. The overall mean PMC index was 6.67, indicating an above-average global policy quality. High scores were found in policy nature, evaluation, and perspective, while relatively low scores were found in policy content and policy tools. These findings reveal a structural imbalance characterized by comprehensive strategic frameworks but weak implementation instruments. Global medical AI policies demonstrate varying degrees of internal consistency and operational strength. Most exhibit robust strategic orientation but insufficient regulatory and ethical detailing. China’s policy framework should prioritize refining data governance mechanisms, ethical accountability, and dynamic policy adjustment systems to enhance implementation capacity and foster the translation of medical AI technologies into clinical practice.

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Publication Details

Journal
BMC Health Services Research
Published
2026-10-05
DOI
https://doi.org/10.1186/s12913-026-15763-w
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Evaluation of global national-level medical artificial intelligence policies and strategies based on the policy modeling consistency index model

Xing Qu, Yiwen Xu, Siqi Huang, Fei Wang et al.
BMC Health Services Research
Artificial Intelligence in Healthcare and Education
article

Evaluation of global national-level medical artificial intelligence policies and strategies based on the policy modeling consistency index model

Xing Qu, Yiwen Xu, Siqi Huang, Fei Wang, Jin Wen
article en

Abstract

The rapid advancement of artificial intelligence (AI) technologies has profoundly transformed the healthcare sector. Governments in major economies have incorporated medical AI into national strategies or policies, issuing policies to guide innovation, regulation, and ethical use. This study aims to quantitatively evaluate the quality of medical AI policies across major countries and regions using the Policy Modeling Consistency (PMC) Index model, providing empirical evidence to inform the optimization of national level medical AI policy framework. Based on the PMC Index model, a multidimensional evaluation system comprising nine primary and thirty-five secondary variables was constructed to assess the internal consistency, structural completeness, and implementation strength of medical AI policies. Forty-two representative policy documents released between 2019 and 2025 from the United States, the United Kingdom, Japan, South Korea, Australia, China, and the European Union were analyzed through text mining and quantitative modeling. The PMC index and concavity index were calculated to classify policy quality into five grades (A–E) and visualize their multidimensional strengths and weaknesses using PMC surface mapping. Among the 42 policies analyzed, 2 (4.76%) were rated Grade A, 29 (69.05%) were Grade B, 9(14.51%) were Grade C, and 2 (4.76%) Grade D. The overall mean PMC index was 6.67, indicating an above-average global policy quality. High scores were found in policy nature, evaluation, and perspective, while relatively low scores were found in policy content and policy tools. These findings reveal a structural imbalance characterized by comprehensive strategic frameworks but weak implementation instruments. Global medical AI policies demonstrate varying degrees of internal consistency and operational strength. Most exhibit robust strategic orientation but insufficient regulatory and ethical detailing. China’s policy framework should prioritize refining data governance mechanisms, ethical accountability, and dynamic policy adjustment systems to enhance implementation capacity and foster the translation of medical AI technologies into clinical practice.

BMC Health Services Research
Sichuan University (CN), West China Hospital of Sichuan University (CN)
Openalex Percentile: Top 18%
Artificial Intelligence in Healthcare and Education
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