Perceptions of dental students on the integration of artificial intelligence in radiology clinical education

Abstract

Objective: To assess dental students' perceptions of artificial intelligence (AI) in radiology education, focusing on diagnostic value, curriculum preparedness, and faculty support. Methods: = 66, response rate 71.7%) at the University of Florida College of Dentistry after exposure to the Overjet Caries Assist (OCA) platform (Overjet Inc. Claymont, DE, USA). Likert-scale, multiple-choice, and open-ended items captured attitudes toward diagnostic accuracy, skill development, curriculum integration, and patient communication. Descriptive statistics, polychoric correlations with bootstrap resampling, and thematic analysis of qualitative responses were conducted. Results: Most students reported that AI improved their ability to detect caries (89.4%) and enhanced radiographic interpretation (92.4%). However, only 16.7% agreed the curriculum adequately prepared them to use AI clinically, and just 45.5% felt confident about integrating AI into future practice. Open-ended feedback highlighted three themes: 1) need for structured faculty training, 2) earlier and more frequent AI exposure, and 3) emphasis on mitigating automation bias, or the over reliance on technology and automated systems in clinical judgement. Correlation analysis revealed strong associations between improved interpretation, skill development, and patient communication (r > 0.80), however, significant negative correlations emerged between student outcomes and perceptions of faculty preparedness. Conclusions: Students value AI as a diagnostic learning aid but identify gaps in curricular structure and faculty calibration. A structured, faculty-led AI curriculum introduced early in training and paired with patient communication strategies may optimize preparedness while safeguarding critical thinking.

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