Ready for robotic dentistry? a comparative study of trust, behavioral intention, and procedural acceptance
Artificial intelligence and robotic systems are becoming part of dental care, but it remains unclear to what extent dentists, dental students, and non-dental participants would accept receiving robot-assisted dental treatment themselves and how much they trust these technologies. This cross-sectional online survey included 1,055 participants: 542 dental students, 95 practicing dentists, and 418 non-dental participants. The questionnaire assessed perceived usefulness, procedural acceptance, trust and control, social influence, ethics, privacy, and cost, and behavioral intention. All composite scores were calculated on 5-point Likert-type scales, with higher scores indicating greater agreement or acceptance. Data were analyzed using descriptive statistics, group comparisons, the Friedman test, correlation analyses, and hierarchical linear regression. Participants generally considered AI and robotic systems useful, but their willingness to undergo robot-assisted dental procedures was relatively low. Mean scores were 2.95 for perceived usefulness and 2.25 for procedural acceptance. Acceptance differed significantly across the 11 dental procedures, with the highest score for tooth whitening and the lowest for soft-tissue surgery. Dentists showed lower procedural acceptance than both dental students and non-dental participants. The trust and control construct was the strongest predictor of behavioral intention, followed by procedural acceptance and perceived usefulness. The final regression model explained 56.5% of the variance in behavioral intention. Acceptance was generally lower for more invasive procedures, particularly surgical procedures. Acceptance of AI and robotic dentistry was associated more strongly with trust, clinician control, and procedure type than with perceived usefulness alone. Further cross-cultural studies involving multiple countries are needed to confirm these findings in more diverse populations.
Authors
- Gökçe Naz Cömert (ORCID: https://orcid.org/0000-0003-0157-2871)
- Arda Arısan (ORCID: https://orcid.org/0009-0004-8920-8605)
- Derya Sağıroğlu
Institutions
- Çanakkale Onsekiz Mart Üniversitesi (TR)
- Ankara Medipol Üniversitesi
Publication Details
- Journal
- BMC Oral Health
- Published
- 2026-09-29
- DOI
- https://doi.org/10.1186/s12903-026-09992-y
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00