Artificial intelligence anxiety and associated factors among clinical nurses in tertiary hospitals in China: a latent profile analysis

Clinical nurses are central to the implementation of medical AI. As AI becomes increasingly integrated into clinical practice, related uncertainties and professional challenges may give rise to varying levels of anxiety. Identifying latent profiles of AI anxiety may help inform targeted support strategies. A cross-sectional design with convenience sampling was employed. From March to May 2026, 516 clinical nurses from four tertiary Grade A hospitals in China completed a sociodemographic questionnaire, the Artificial Intelligence Anxiety Scale, the Medical Artificial Intelligence Readiness Scale, and the Career Resilience Scale. Latent profile analysis classified AI anxiety, and multinomial logistic regression assessed associated factors. Four AI anxiety profiles emerged: “Low AI anxiety” ( n = 79, 15.31%); “Low-to-moderate AI anxiety” ( n = 155, 30.04%); “Moderate-to-high AI anxiety” ( n = 159, 30.81%); and “High AI anxiety” ( n = 123, 23.84%). Multinomial logistic regression revealed that professional title, attitude toward AI application, medical AI readiness, and cognitive career resilience were significantly associated with AI anxiety profile membership. Among nurses recruited from four tertiary Grade A hospitals, substantial variation in AI anxiety severity was observed among clinical nurses, with more than half classified into the moderate-to-high or high AI anxiety profiles. Higher medical AI readiness and cognitive career resilience were associated with lower odds of membership in higher-anxiety profiles. These findings may inform the future development and evaluation of tiered AI education, psychological support, ethical guidance, and human-AI collaboration strategies.