To evaluate general attitudes toward artificial intelligence (AI) among patients attending an oral and maxillofacial surgery clinic and to investigate factors associated with these attitudes.

A cross-sectional survey was conducted among 200 adult patients attending the Department of Oral and Maxillofacial Surgery at Hacettepe University Faculty of Dentistry. Participants completed a demographic questionnaire and the General Attitudes toward Artificial Intelligence Scale (GAAIS). Group comparisons were performed using non-parametric tests, and multivariable linear regression analysis was conducted to identify factors associated with GAAIS scores.

The mean age of participants was 34.2 ± 11.8 years, and 52.5% were female. More than half of the participants (55.5%) reported a moderate level of self-perceived AI knowledge, and 78.0% were willing to accept AI-assisted dental treatment. The mean GAAIS score was 60.44 ± 4.01, indicating relatively favorable attitudes toward AI. Participants willing to accept AI-assisted treatment demonstrated significantly higher GAAIS scores than those unwilling in the univariable analysis (p = 0.006). Willingness to accept AI-assisted treatment remained independently associated with higher GAAIS scores after multivariable adjustment (B = 1.53, p = 0.028), although the overall model explained a limited proportion of variance (adjusted R² = 0.020). No significant independent associations were observed for age, sex, educational level, previous AI use, or self-perceived AI knowledge.

Patients attending an oral and maxillofacial surgery clinic demonstrated relatively favorable attitudes toward AI, and 78.0% were willing to accept AI-assisted dental treatment. Willingness to accept AI-assisted dental treatment was associated with more positive general attitudes toward AI in both univariable and multivariable analyses, although the multivariable model explained only a limited proportion of the variance in GAAIS scores. These findings highlight the importance of transparent patient communication and clinician oversight during the implementation of AI-assisted technologies in oral and maxillofacial surgery. However, the relatively high educational level of this single-center sample may limit the generalizability of these findings to broader dental and OMFS patient populations.