AI-Driven Diagnostic Precision in Endodontics: From Radiographic Interpretation to Predictive Treatment Outcomes

Abstract

The integration of Artificial Intelligence (AI) into endodontics has transformed diagnostic accuracy and treatment planning, offering a new paradigm for precision-based dental care. This study explores the application of AI-driven systems in enhancing diagnostic precision, particularly in radiographic interpretation and the prediction of treatment outcomes. Using advanced algorithms such as convolutional neural networks (CNNs) and deep learning models, AI can effectively detect periapical lesions, identify root canal morphology, and assess treatment prognosis with higher consistency compared to traditional diagnostic approaches. The research highlights how AI facilitates automated image segmentation and interpretation of radiographs and cone-beam computed tomography (CBCT) scans, thereby minimizing human error and improving early detection of pathologies. Furthermore, predictive analytics derived from AI models enable clinicians to anticipate treatment success and potential complications. Despite challenges related to data quality, model transparency, and clinical integration, AI demonstrates significant potential to support endodontic decision-making, streamline diagnostic workflows, and enhance patient outcomes. The findings emphasize the need for continuous model validation and clinician training to ensure ethical, accurate, and reliable AI deployment in endodontic practice.