Artificial Intelligence in Endodontic Diagnostics: Current Applications, Clinical Performance, Limitations, and Future Perspectives—A Narrative Review
Artificial intelligence (AI) has emerged as a promising tool in endodontic diagnostics, particularly in radiographic interpretation and treatment outcome prediction. This narrative review provides a comprehensive overview of current applications of machine learning (ML) and deep learning (DL) algorithms in endodontics, focusing on periapical lesion detection, assessment of root canal morphology, prognosis prediction, and navigation-guided procedures. A literature search was conducted in PubMed, Scopus, and Web of Science for studies published between 2021 and 2026. Following qualitative screening, 45 studies were included in the evidence synthesis. Given the narrative design of the review, no formal meta-analysis was performed. The reviewed evidence indicates that convolutional neural networks (CNNs), U-Net architectures, YOLO-based systems, and other AI models have demonstrated promising diagnostic performance across multiple imaging modalities, with the highest performance generally reported for CBCT-based applications. However, the current body of evidence remains limited by retrospective study designs, small datasets, methodological heterogeneity, and insufficient external validation. Overall, AI should be considered a valuable adjunctive decision-support tool in endodontic diagnostics rather than a replacement for clinical expertise. Further prospective multicenter studies, external validation, and standardized methodological frameworks are required to support its reliable integration into routine clinical practice.