Artificial intelligence predicts healthcare workers' antibiotic use intentions from psychological and behavioral measures across multiple theories.

Antimicrobial resistance (AMR) remains a global health priority, partly driven by non-guideline-concordant antibiotic prescribing among healthcare workers. Existing interventions often overlook the psychological and contextual factors that shape prescribing behaviour. This study integrated behavioural theory with explainable machine learning to identify psychological predictors of intention to use antimicrobials appropriately among clinicians. A cross-sectional survey was conducted among 1135 healthcare workers from four public hospitals in China. Participants completed questionnaires based on constructs from the Theory of Planned Behavior, the Health Belief Model, the Theory of Reasoned Action (TRA), Self-Efficacy Theory, Social Support Theory, and cognitive processing frameworks. LASSO regression and SHAP analysis were applied alongside machine-learning classifiers (e.g., XGBoost, LightGBM, CatBoost) to identify key predictors and interactions influencing intention to use antimicrobials appropriately. Social support, cognitive processing, knowledge and skills, and health beliefs were the most important predictors. SHAP analysis revealed nonlinear interactions, particularly between social support and cognitive processing. Ensemble models achieved high predictive accuracy (F1-scores > 0.94) for high and medium intention to use antimicrobials appropriately, whereas classifying low-intention respondents remained more challenging. Combining behavioural theory with explainable AI offers a scalable approach to identifying clinicians at risk of non-guideline-concordant prescribing. These findings support the development of psychologically tailored, real-time interventions to strengthen antibiotic stewardship and address AMR across diverse health-system settings.