ArticleJMIR medical informatics2026
Acceptance of Artificial Intelligence in Clinical Practice Among Chinese Physicians: Nationwide Cross-Sectional Survey Using Extended Unified Theory of Acceptance and Use of Technology and Explainable Machine Learning.
Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is rapidly transforming clinical practice, yet empirical evidence on Chinese physicians' acceptance of AI medical tools remains scarce at the national level. Objective: This study aimed to evaluate the current acceptance of AI medical tools among Chinese physicians, identify key determinants, and elucidate underlying mechanisms using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) and explainable machine learning. Methods: A nationwide cross-sectional survey was conducted from January to April 2024, recruiting 4024 in-service physicians across 29 provincial-level administrative units in China via stratified random sampling. The questionnaire incorporated 5 UTAUT constructs-performance expectancy, effort expectancy, social influence, facilitating conditions (FC), and a newly introduced "positive impact" dimension. Psychometric properties were validated through exploratory and confirmatory factor analyses. Structural equation modeling assessed direct and moderated effects, with hospital level, professional title, AI familiarity, and future optimism as moderators. Six classification models were compared for predictive performance; balanced random forest was selected, and model interpretability was evaluated using Shapley Additive Explanations (SHAP). Results: Overall acceptance exceeded 90% across subgroups. Structural equation modeling showed that performance expectancy, social influence, FC, and positive impact significantly and positively predicted physicians' behavioral intention to use AI medical tools. Six negative moderation effects were identified. The random forest achieved 85.6% accuracy and an area under the receiver operating characteristic curve of 0.836; SHAP analysis identified organizational support (FC_HospPromoteAI) as the feature with the highest mean absolute SHAP value, though all effect sizes were modest. Conclusions: Chinese physicians exhibit high acceptance of AI medical tools, mainly driven by organizational support and perceived clinical benefits. The combined use of extended UTAUT and explainable AI provides actionable insights for targeted AI implementation strategies in health care.
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Registered trials
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