Dental caries remains one of the most widespread chronic diseases worldwide and continues to impose a substantial burden on public health systems. Early and accurate diagnosis is essential for effective caries management, prevention of lesion progression, and preservation of dental tissues. Conventional diagnostic approaches, including visual-tactile examination and radiographic assessment, have long been considered the clinical standard; however, these methods present inherent limitations related to subjectivity, operator dependency, and reduced sensitivity for early-stage lesions.

Rapid advances in digital technologies and artificial intelligence (AI) have introduced innovative diagnostic tools aimed at improving accuracy, reproducibility, and early detection of dental caries. AI-based systems, utilizing machine learning and deep learning algorithms, offer automated image analysis and objective lesion classification, potentially overcoming the limitations of traditional methods.

The aim of this study was to comprehensively evaluate the role of digital diagnostic technologies in caries detection and to compare their diagnostic performance with conventional methods. Particular attention was given to diagnostic accuracy, sensitivity for early lesions, clinical applicability, and potential public health implications. The findings indicate that AI-assisted diagnostic systems demonstrate higher sensitivity and consistency in detecting early carious lesions, while traditional methods maintain high specificity. Integration of digital and AI technologies into routine dental practice may significantly enhance early caries detection and support minimally invasive treatment strategies.

dental caries; digital diagnostics; artificial intelligence; caries detection; diagnostic accuracy; preventive dentistry; public health

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