Readiness of Dental Professionals to Adopt Artificial Intelligence for Cone-Beam Computed Tomography Interpretation: A Cross-Sectional Survey.

INTRODUCTION: Artificial intelligence (AI) is being increasingly incorporated into cone-beam computed tomography (CBCT) interpretation to support diagnostic accuracy and clinical decision-making. However, successful implementation depends on the readiness and acceptance of the dental professionals. This study aimed to evaluate the readiness of dental professionals to adopt AI for CBCT interpretation and identify the demographic and professional factors associated with AI readiness. MATERIALS AND METHODS: A cross-sectional questionnaire-based survey was conducted among dental professionals using a structured questionnaire for demographic information (section 1) and AI readiness assessment (section 2). The questionnaire was developed by a multidisciplinary panel of dental specialists, and pilot-tested and assessed for internal consistency before administration. Responses were collected electronically on a five-point Likert scale. Descriptive statistics were used to summarize the participant characteristics and readiness scores. Internal consistency was evaluated using Cronbach's alpha. Independent t-tests, Spearman's rank-order correlation, and multiple linear regression analyses were performed to identify the factors associated with AI readiness. RESULTS: A total of 380 complete responses were included in the analysis. The questionnaire demonstrated good internal consistency (Cronbach's α = 0.87). The largest proportion of participants belonged to the age group of 31-40 years, followed by the age group of 20-30 years; 209 (55.0%) participants were male. Most respondents were Master in Dental Surgery (MDS) graduates, and 114 (30.0%) had 5-10 years of clinical experience. Younger participants (≤40 years), qualified postgraduate professionals, those with <10 years of clinical experience, and academic practitioners demonstrated significantly higher AI readiness scores than their counterparts (p < 0.05). Correlation analysis showed that age (r = -0.34, p < 0.001) and clinical experience (r = -0.32, p < 0.001) were negatively associated with AI readiness, whereas qualification demonstrated a positive correlation (r = 0.28, p < 0.001). Multiple linear regression revealed that greater clinical experience independently predicted lower readiness (β = -0.28, p < 0.001), whereas higher qualification (β = 0.15, p = 0.002), academic practice setting (β = 0.10, p = 0.042), and prior AI training (β = 0.17, p = 0.001) were significant positive predictors. The regression model explained 25% of the variance in AI readiness (R² = 0.25; adjusted R² = 0.24; p < 0.001). CONCLUSION: Dental professionals demonstrated an encouraging level of readiness to adopt AI for CBCT interpretation, although acceptance varied according to demographic and professional characteristics. Educational qualification, professional experience, and prior AI exposure were seen to be important determinants of readiness. Incorporating structured AI education and continuing professional training into dental curricula and clinical practice may facilitate the integration of AI-assisted CBCT interpretations, improve diagnostic efficiency, and support evidence-based patient care.