Artificial Intelligence in Caries Detection: Accuracy and Clinical Integration

Abstract

Dental caries remains one of the most prevalent chronic diseases worldwide, with early detection being critical for effective
prevention and treatment. Conventional diagnostic methods, including visual-tactile examination and radiography, are
often limited by subjectivity and variability in interpretation. Recent advances in artificial intelligence (AI), particularly
deep learning and convolutional neural networks, have shown promising performance in enhancing diagnostic accuracy
for caries detection. Studies published between 2020 and 2021 demonstrate that AI systems can achieve accuracy levels
comparable to, and in some cases exceeding, those of experienced clinicians, particularly in the detection of early
lesions. Beyond accuracy, the integration of AI into clinical workflows offers opportunities for chairside decision support,
improved patient education, and expanded access through teledentistry. However, challenges remain, including the need
for large annotated datasets, clinician acceptance, regulatory approval, and interoperability with existing dental practice
management systems. This review highlights the current evidence on AI accuracy in caries detection, discusses barriers
and facilitators to clinical integration, and outlines future directions for AI-enhanced preventive and restorative dentistry.