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.