Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey.

OBJECTIVES: To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, and experiences regarding medical artificial intelligence (AI), and to explore and compare the factors influencing AI behavioral intention (BI; willingness to adopt AI) between physicians and nurses using the Unified Theory of Acceptance and Use of Technology (UTAUT). MATERIALS AND METHODS: A nationwide cross-sectional survey was conducted among professionals in oncology departments across China. Sociodemographic characteristics, awareness, perceptions, and user experiences related to medical AI were collected. The UTAUT was specifically applied to the third section of the survey to evaluate participants' perceptions. Based on this framework, structural equation modeling (SEM) was performed to examine associations between BI and 6 latent constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), perceived risk (PR), and trust (TR) based on the UTAUT. Subgroup SEM analyses were then conducted for physicians and nurses. RESULTS: Six hundred ten valid responses were included in the final analysis, consisting of 188 (30.8%) physicians and 422 (69.2%) nurses. Only 17.7% reported having both heard of and used medical AI, while 67.7% had heard of it but never used it. SEM results showed that TR (confidence in system reliability; β = .668, P < .001) had the strongest effect on BI, followed by effort EE (perceived ease of use; β = .155, P = .026). Subgroup analyses revealed that for physicians, both EE (β = .257, P = .038) and TR (β = .486, P < .001) significantly influenced BI, while among nurses, only TR (β = .729, P < .001) showed a significant effect. CONCLUSIONS: There is a significant "awareness-usage gap" among Chinese oncology professionals, with high awareness but low practical integration of medical AI. This nationwide study indicates that TR and EE are associated with the intention to use AI. To bridge this gap, hospital management should implement differentiated training and local validation protocols that address the unique technical and ethical concerns of physicians and nurses, respectively.