Evaluation of the Diagnostic and Prognostic Accuracy of Artificial Intelligence Algorithms in Caries Detection and Endodontic Diagnosis: A Systematic Review

Aim To evaluate the diagnostic and prognostic performance of artificial intelligence (AI) models in caries detection and endodontic diagnosis. Materials and Methods A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and registered with PROSPERO (CRD42023445906). Six databases (PubMed, Scopus, Web of Science, Cochrane Library, ScienceDirect, and Wiley) were searched for studies published from January 1, 2001 to September 30, 2025. Eligible designs included randomized controlled trials and observational studies assessing AI for diagnostic accuracy in caries detection or endodontic conditions, and the prognostic prediction of treatment outcomes. Primary outcomes were diagnostic accuracy, sensitivity, specificity, and prognostic validity. Risk of bias was assessed using ROBINS-I. Results From 455 records, 68 studies were included; most evidence is retrospective and experimental. Convolutional neural networks (CNNs), U-Net, ResNet, and YOLO models demonstrated diagnostic accuracies ranging from 86.9% to 95% (95% CI where reported). Sensitivity for periapical lesion identification reached 97.8%, and precision for root morphology analysis and working length determination exceeded 90% in several studies. Prognostic applications showed potential in predicting retreatment success and supporting clinical decisions, though heterogeneity limited pooled analysis. External validation was rarely performed. Conclusion AI models show promising diagnostic and prognostic accuracy in caries and endodontic applications, with consistently high performance across tasks. However, most studies were limited by narrow datasets, methodological variability, and lack of external validation. Current evidence supports cautious optimism, but further high-quality trials and real-world validation are needed before widespread clinical adoption.