<![CDATA[Artificial intelligence (AI) has become one of the most rapidly developing technologies in modern healthcare, significantly influencing the field of digital dentistry and particularly endodontics. The integration of AI-based systems into endodontic diagnostics has created new opportunities for improving the precision, speed, and reliability of clinical decision-making processes. Conventional diagnostic approaches in endodontics often depend on the clinician’s experience and interpretation of radiographic images, which may lead to diagnostic variability and delayed detection of pathological changes. In this regard, artificial intelligence technologies, including machine learning and deep learning algorithms, are increasingly being utilized to enhance the diagnostic performance of digital radiographic systems such as cone-beam computed tomography (CBCT), periapical radiography, and other imaging modalities. The present study aims to analyze the role of artificial intelligence in improving diagnostic accuracy in digital endodontics and to evaluate its advantages, limitations, and future prospects in clinical practice. The article reviews contemporary scientific literature related to AI-assisted diagnostic systems in endodontics, with particular attention to the detection of periapical lesions, root canal morphology, vertical root fractures, pulp pathologies, and treatment planning. Recent investigations indicate that AI-supported diagnostic software demonstrates high sensitivity and specificity in identifying endodontic diseases, thereby reducing human error and contributing to more accurate therapeutic strategies. Furthermore, the study highlights the growing importance of digital technologies in modern dental education and clinical management. The implementation of AI in endodontics not only improves radiographic interpretation but also optimizes workflow efficiency, minimizes diagnostic inconsistencies, and supports evidence-based treatment approaches. Despite its numerous advantages, several challenges remain, including data privacy concerns, ethical considerations, algorithm transparency, and the need for standardized clinical validation]]>
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