Abstract
INTRODUCTION AND AIMS: Artificial intelligence (AI) is transforming dental care by enhancing diagnostic accuracy, efficiency, and patient experience. This study aimed to assess dental patients' acceptance, perceptions, and concerns regarding AI-powered diagnosis using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework through structural equation modelling (SEM). METHODS: A cross-sectional study was conducted among dental patients at King Saud University Dental Hospital, Riyadh. Data were collected using a structured questionnaire based on the UTAUT2 framework. Confirmatory factor analysis was performed to assess the validity and reliability of the measurement model. SEM was then utilized to evaluate the relationships between UTAUT2 constructs and patients' behavioural intention toward AI-powered dental diagnosis. RESULTS: A total of 505 participants responded to the questionnaire. SEM indicated a strong model fit (GFI = 0.900, Comparative Fit Index = 0.906, Tucker-Lewis Index = 0.907, root mean square error of approximation = 0.083). Performance expectancy (β = 0.32, P < .001), social influence (β = 0.56, P < .001), and facilitating conditions (β = 0.34, P < .001) significantly predicted behavioural intention. The model explained 68.1% of the variance (R² = 0.681), highlighting key drivers of AI adoption. While 60% of participants trusted AI dental diagnosis, 55% expressed concerns about data privacy and accuracy. CONCLUSION: This study demonstrates that performance expectancy, social influence, and facilitating conditions are key factors influencing the acceptance of AI in dental diagnosis. Addressing these factors can help develop user-friendly AI tools in dentistry, thereby improving adoption. Future research should focus on clear communication about AI and patient education to alleviate concerns and promote equitable and widespread use. CLINICAL RELEVANCE: Acceptance of AI in dentistry depends on performance, motivation, and trust. Understanding these factors can guide the user-centred development of AI technologies, enhancing their integration into diagnostics and improving patient outcomes.