USING ARTIFICIAL INTELLIGENCE FOR EARLY DIAGNOSIS OF CARIES
DOI:
https://doi.org/10.11603/2311-9624.2026.2.16436Keywords:
artificial intelligence, dental caries, early diagnosis, digital dentistry, dental imaging, deep learningAbstract
Dental caries remains one of the most common dental pathologies, and its early detection is an important prerequisite for the timely use of preventive and minimally invasive interventions. With the development of digital dentistry, growing attention is being paid to the use of artificial intelligence technologies for the analysis of dental images and support of the diagnostic process.
Objective. To analyze current literature data on the use of artificial intelligence for early caries detection and to determine its advantages, limitations, and prospects for application in clinical dentistry.
Materials and methods. An analysis of current scientific publications indexed in PubMed and Google Scholar was conducted. The search strategy was based on clinical and technological blocks of keywords.
Results and discussion. According to current studies, artificial intelligence tools demonstrate high diagnostic effectiveness in caries detection, especially in the analysis of dental radiographs. Their most appropriate use is as a “second reader” to increase the sensitivity of detecting early approximal lesions and to standardize image assessment. At the same time, the effectiveness of such systems depends on the type of caries, lesion localization, image characteristics, and the features of the training dataset. The main limitations include the risk of false-negative and false-positive results, ethical and legal issues, data confidentiality concerns, and the high cost of implementation.
Conclusions. Artificial intelligence is a promising auxiliary tool for early caries detection; however, at the present stage it cannot be considered an independent means of establishing a diagnosis and should be used in combination with the clinical experience of the dentist.
Key words: artificial intelligence, dental caries, early diagnosis, digital dentistry, dental imaging, deep learning.
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