BMC Med Educ. 2026 Jul 6. doi: 10.1186/s12909-026-09878-1. Online ahead of print.
ABSTRACT
INTRODUCTION: Digitalization, which is rapidly increasing its influence across many fields, has also led to a significant transformation in dentistry, evolving into a fundamental component that dentists are expected to master alongside traditional practices. Within this transformation in digital dentistry, teledentistry and artificial intelligence applications have emerged as prominent areas of focus. This study aimed to evaluate dental students’ knowledge, attitudes, and perceptions regarding teledentistry and artificial intelligence-supported systems. It also investigated their views on the future applications of these technologies and compared the awareness and expectations of preclinical and clinical students.
METHODS: This descriptive cross-sectional study was conducted among dentistry students at the Faculty of Dentistry, Sakarya University, including both preclinical and clinical levels. Data were collected using an 18-item structured questionnaire developed by the researchers and administered online via Google Forms. The internal consistency of the questionnaire was assessed using Cronbach’s Alpha, and multiple linear regression analysis was performed to identify predictors of teledentistry knowledge levels. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22.0. Descriptive statistics (frequencies and percentages) were calculated, and Chi-square tests were used to compare categorical variables. A p-value of < 0.05 was considered statistically significant.
RESULTS: A total of 368 students participated in the study, of whom 237 were female and 131 were male; 156 were preclinical and 212 were clinical students. Clinical students demonstrated significantly higher knowledge regarding the purpose of artificial intelligence-based system usage in teledentistry compared with preclinical students (p < 0.05). Additionally, a significant difference was found in the sources of information about teledentistry between the two groups (p < 0.05). Social media was the most common information source for both groups, while a higher proportion of preclinical students reported having no prior knowledge of teledentistry (64.7%). Most participants identified artificial intelligence and big data analytics as the most influential technologies for the future development of teledentistry.
CONCLUSIONS: Integrating teledentistry and artificial intelligence more extensively into undergraduate dental education may enhance future dentists’ competence in digital dentistry and better prepare them to adapt to rapidly evolving technological advancements in the profession. The findings also indicate that dental students generally demonstrate positive attitudes toward AI-supported teledentistry despite having limited knowledge levels, with clinical students showing higher levels of awareness compared with preclinical students.
PMID:42410419 | DOI:10.1186/s12909-026-09878-1
