Determinants of the Public's Behavioral Intention to Adopt AI-Assisted Lung Cancer Screening: An Extended UTAUT Model...

Background: The integration of artificial intelligence (AI) into lung cancer screening offers significant potential; however, public adoption of AI-assisted lung cancer screening remains inconsistent and poorly understood. A robust understanding of the psychological and social determinants underlying adoption is critical to inform evidence-based implementation strategies. Objective: This study aims to identify the key factors that influence the public's Behavioral Intention (BI) to adopt AI-assisted lung cancer screening. We built on the Unified Theory of Acceptance and Use of Technology (UTAUT) and integrated Doctor-Patient Trust and Perceived Risk into the framework to examine the associations between these medically specific factors, together with traditional adoption variables, and the public's BI. Methods: A cross-sectional survey was conducted among 971 residents in China from September to November 2025. Based on the extended UTAUT, a measurement instrument was developed and refined through expert consultations and pilot testing. Exploratory factor analysis (EFA) was performed to validate the questionnaire's construct validity. Hypothesis testing was then carried out via Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and examine the structural relationships among latent constructs. Results: EFA results indicated a seven-factor structure (KMO = 0.897, p < 0.001). The structural model accounted for 35.0% of the variance in BI. Social Influence (β = 0.292, p < 0.001), Facilitating Conditions (β = 0.156, p < 0.001), Performance Expectancy (β = 0.101, p = 0.004), Doctor-Patient Trust (β = 0.107, p = 0.002) were positively associated with BI, while Perceived Risk (β = -0.106, p < 0.001) showed a negative association. Furthermore, Doctor-Patient Trust was significantly and negatively associated with Perceived Risk (β = -0.168, p < 0.001), suggesting a potential mediating pathway from trust to intention (Indirect Effect = 0.018, p = 0.003). Conclusions: Adoption of AI-assisted lung cancer screening appears to be associated not only with perceived utility but also with trust in medical professionals and Perceived Risk. These findings suggest the importance of integrating technological innovation with strategic public education and tailored communication strategies to foster its adoption. Public health interventions should leverage physician endorsements and promote AI awareness to support informed, trust-based engagement with AI technologies.