Awareness, Attitudes, and Barriers Toward Artificial Intelligence in Dentistry: A Cross-Sectional Survey of Dental Professionals in Kuwait

Background/Objectives: Artificial intelligence (AI) is rapidly entering dental education and clinical practice, yet data on how dental professionals in the Middle East perceive it are scarce. This study assessed the awareness, attitudes, and perceived barriers toward AI among dental professionals in Kuwait, and compared responses across specialty, work sector, place of training, and experience, in order to inform regional education and adoption strategies. Methods: A cross-sectional online survey of 27 items across five domains (demographics, knowledge and awareness, clinical practice, attitudes, and barriers and concerns) was distributed to dental care providers practicing in Kuwait’s government, private, and academic sectors between January 2024 and January 2025. An investigator-defined composite “belief” score (range 0–9), calculated as the sum of affirmative responses to nine attitude and expectation items (Kuder–Richardson 20 = 0.79), was categorized a priori as low (0–4), moderate (5–7), or high (8–9). This score was constructed for the present study to summarize overall positive orientation toward AI and is not a previously validated instrument. Categorical data were compared using the chi-square or Fisher exact test, and ordinal logistic regression was used to identify predictors of a higher belief-score category. Results: A total of 321 dental professionals accessed the survey, and 300 with complete belief-score data formed the analytic sample. Just over half were aware of AI tools designed for dentistry (56.3%), yet exposure was limited: only 38.7% had attended an AI-related lecture, workshop, or webinar, 29.0% had read AI research, and 15.7% had used AI-assisted tools, while 94.0% were interested in AI training and 92.0% were open to adopting AI if beneficial. Among the 292 respondents who answered the multiple-response barrier item, the most frequently reported barriers were a lack of training and expertise (71.2%), technical challenges (63.4%), and cost (56.5%); these did not differ significantly across work sector or specialty. High composite beliefs were more common among male than female respondents (44.1% vs. 25.3%, p = 0.003), and in ordinal regression male gender (estimate = 0.736, p = 0.004), general dentistry (estimate = 0.924, p = 0.003), and prosthodontics (estimate = 1.120, p = 0.003) independently predicted higher beliefs (model χ2 = 25.56, df = 9, p = 0.002). Conclusions: Dental professionals in Kuwait show high enthusiasm for AI but limited training and hands-on exposure, and a lack of training and expertise was the leading perceived barrier (71.2% of the 292 respondents answering this item) across all sectors and specialties. Targeted, hands-on AI training and the integration of AI content into dental curricula are needed to support effective and responsible adoption in Kuwait.