Artificial Intelligence in Dental Caries Detection: Current Advances, Challenges, and Future Perspectives
https://doi.org/10.5281/zenodo.20364997
DOI:
https://doi.org/10.66021/pakmcr1092Abstract
Dental caries remains one of the most prevalent chronic oral diseases worldwide, posing a significant burden on public health systems and affecting individuals across all age groups. Conventional diagnostic approaches, including visual-tactile examination and radiographic assessment, are often limited by subjectivity, variability, and reduced sensitivity in detecting early-stage lesions. In recent years, artificial intelligence (AI), particularly machine learning and deep learning techniques, has emerged as a promising tool to enhance the accuracy and efficiency of caries detection. This review provides a comprehensive overview of current advancements in AI applications for dental caries diagnosis, focusing on deep learning models such as convolutional neural networks and their role in analyzing dental radiographs and clinical images. The review highlights the clinical applications of AI in detecting proximal, occlusal, and early enamel lesions, as well as its potential in caries risk prediction and integration into digital dental workflows. Furthermore, the advantages of AI, including improved diagnostic consistency, early detection, and workflow optimization, are discussed alongside key challenges such as data quality, lack of standardization, ethical concerns, and limited interpretability. Emerging trends, including explainable AI, teledentistry integration, and real-time chairside diagnostic tools, are also explored. Overall, AI demonstrates substantial potential to transform caries diagnosis and support precision dentistry; however, further validation, standardization, and clinical integration are essential for its widespread adoption in routine dental practice.
