Artificial Intelligence as a Clinical Decision-Support System in Endodontic Retreatment: Challenges and Clinical Implications

Abstract

Artificial Intelligence (AI) is increasingly transforming dental practice, particularly in endodontics, by serving as a clinical decision-support system (CDSS). In endodontic retreatment, treatment planning and prognosis are often complex due to anatomical variations, prior interventions, and the risk of treatment failure. AI technologies, including machine learning and deep learning algorithms, have shown promise in assisting clinicians with accurate diagnosis, risk assessment, and evidence-based treatment planning. However, the integration of AI in clinical practice faces several challenges, including limited high-quality data, ethical and legal concerns, workflow compatibility, and clinician acceptance. Despite these hurdles, the potential clinical implications are substantial, with AI offering enhanced diagnostic accuracy, improved treatment outcomes, and more efficient clinical decision-making. This review explores the current applications, challenges, and future directions of AI as a CDSS in endodontic retreatment, emphasizing the need for validated models and standardized protocols to ensure safe and effective clinical adoption.