Effect of AI-assisted caries annotation on dental students’ performance in caries detection on panoramic radiographs

BACKGROUND: Dental caries remains one of the most prevalent oral diseases globally. The integration of artificial intelligence (AI) into dental radiographic interpretation, particularly for caries detection, has expanded rapidly. AI-assisted caries annotation may help dental students identify carious lesions on panoramic radiographs-a commonly used diagnostic tool for evaluating teeth and surrounding structures-thereby improving diagnostic accuracy, efficiency, and confidence. This study aimed to assess the effect of AI-assisted caries annotation on dental students' diagnostic performance, confidence, and time efficiency in detecting caries on panoramic radiographs. METHODS: Fifty panoramic radiographs with multistage carious lesions, verified by bite-wing radiographs as the gold standard, were randomly selected. Caries were annotated using recently developed AI-assisted software. Forty fourth-year dental students participated after calibration with ten sets of unannotated and AI-annotated radiographs. In Session 1, participants identified carious lesions on 40 unannotated panoramic radiographs. One month later (Session 2), the same radiographs were re-evaluated with AI-assisted caries annotation, alongside an unannotated radiograph. For each radiograph, the location and depth of detected caries, diagnostic time, and self-reported confidence (0-10 scale) were recorded. Diagnostic accuracy, sensitivity, specificity, balanced accuracy, precision, negative predictive value, and miss rate between the sessions were compared using paired t-tests or Wilcoxon tests. RESULTS: AI-assisted caries annotation significantly enhanced diagnostic performance compared with conventional interpretation. Accuracy increased from 0.91 to 0.96, sensitivity from 0.35 to 0.67, specificity from 0.96 to 0.99, balanced accuracy from 0.65 to 0.83, precision from 0.33 to 0.77, negative predictive value from 0.95 to 0.98, while the miss rate decreased from 0.65 to 0.33 (p < 0.001). Students' confidence improved notably for enamel caries (4.0 to 6.0), dentin caries (5.0 to 7.0), and pulp-involved caries (8.0 to 8.5), as well as overall detection (5.0 to 7.0) (p < 0.001). Moreover, the mean diagnostic time per radiograph significantly decreased from 67.89 to 53.92 s (p < 0.001). CONCLUSIONS: AI-assisted caries annotation substantially enhanced diagnostic efficiency, reduced interpretation time, and improved dental students' confidence in detecting dental caries across all depths. These findings highlight the potential of AI-assisted annotation as an effective educational adjunct for developing diagnostic competence in dental radiology.