Understanding Chinese Physicians’ Acceptance of AI in Clinical Practice: A Nationwide Study Based on Extended UTAUT and Explainable Machine Learning (Preprint)

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 4,024 in-service physicians across 29 provincial-level administrative units in China via stratified random sampling. The questionnaire incorporated five UTAUT constructs—performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and a newly introduced “positive impact” (PI) dimension. Psychometric properties were validated through exploratory and confirmatory factor analyses. Structural equation modeling (SEM) 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. SEM showed PE, SI, FC, and PI positively predicted behavioral intention, while EE was non-significant. Six negative moderation effects were identified. The random forest achieved 85.6% accuracy and an AUC of 0.836; SHAP analysis highlighted organizational support as the most important predictor. 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 healthcare. CLINICALTRIAL This study involved a nationwide, anonymous, cross-sectional online survey of in-service physicians. No patient information or personally identifiable data (eg, names, ID numbers, contact details, IP addresses) were collected, and all analyses were conducted on aggregated data. Participation was voluntary and could be discontinued at any time. An electronic informed-consent statement was presented on the first page of the questionnaire; proceeding to the survey indicated consent. The study adhered to the principles of the Declaration of Helsinki. INTERNATIONAL REGISTERED REPORT RR2-83264