Influence of Oral and Maxillofacial Radiology Training and Experience on Attitudes Toward Artificial Intelligence in Dentistry: A Cross-Sectional Questionnaire Study

Objective: To evaluate whether professional status and training level influence attitudes toward artificial intelligence (AI) in dentistry, and to compare present-day AI attitudes with expectations for near-future AI applications. Materials and Methods: This cross-sectional questionnaire study drew on responses from 234 volunteers spanning four professional groups: 63 fourth-year dental students, 78 fifth-year dental students, 47 oral and maxillofacial radiology residents, and 46 oral and maxillofacial radiology specialists/faculty members. Present-attitude items, near-future-attitude items and awareness of selected AI applications were evaluated using multivariable models adjusted for professional status, sex, and age. Results: Internal consistency was high for both the present-attitude subscale (alpha = 0.852) and the near-future-attitude subscale (alpha = 0.872). Near-future ratings exceeded present-day ratings in all five paired domains (all p < 0.001). Professional status/training level was significantly associated with the present-attitude composite score (p = 0.017) and with the change score between near-future and present attitudes (p < 0.001), but not with the near-future composite score in isolation (p = 0.208). Radiology residents showed lower present-attitude scores (β = -2.41) yet a larger change score (β = +1.95) relative to fourth-year students. Conclusions: More experienced radiology groups were found to be relatively more cautious but more aware of certain present-day applications, yet showed a stronger tendency toward accepting near-future AI capabilities-particularly for structured, workflow-oriented tasks. Sustainable AI integration in dentistry may require more than attitudinal readiness-it may depend on grounded familiarity with validated tools and on addressing the practical and economic realities of clinical implementation.