Artificial intelligence assisted panoramic radiography for enhanced caries diagnosis in clinical dental practice

Objectives This study aimed to evaluate the effectiveness of artificial intelligence (AI) panoramic radiography in improving caries detection accuracy in clinical dental practice, with a particular focus on enhancing diagnostic accuracy among early-career dentists. Materials and Methods In this retrospective diagnostic accuracy study, 15 dentists (12 young dentists with ≤ 3 years experience, 3 experienced dentists with > 10 years experience) interpreted 402 panoramic images under four conditions: AI-assisted, unassisted, AI-only, and expert reference. Primary endpoints included tooth-level sensitivity/specificity; secondary outcomes comprised interpretation time, case-level metrics, predictive values, and area under curve (AUC). Ground truth was established by three experienced experts using pixel-wise annotations. Results When assisted by the AI system, young dentists showed a marked improvement in sensitivity (Δ = 15.0%, 82.4% vs 67.4%, P  < 0.001) without compromising specificity (97.4% vs 97.2%), suggesting that AI may help bridge the diagnostic experience gap. Interpretation time was also shortened by 22.7% (50.37 s vs 65.12 s, P  = 0.003). Case-level analysis showed improved sensitivity (93.3% vs 84.7%, P <  0.001) and negative predictive value (93.9% vs 88.0%, P  = 0.008). When operating independently, the AI system achieved 79.2% sensitivity and 98.4% specificity (AUC = 0.938), outperforming unassisted young dentists in both metrics ( P  < 0.05). Conclusions AI-assisted panoramic radiography demonstrated clinically meaningful improvements in diagnostic performance and efficiency, particularly benefiting less experienced dentists.