Artificial Intelligence Tools for Dental Caries Detection: An Exploratory Systematic Review

Background/Objectives: Dental caries is one of the most prevalent oral diseases. Traditionally, the detection and diagnosis have been performed using conventional methods, such as visual inspection and radiographs. In this context, artificial intelligence-based tools have emerged as promising solutions to improve the efficiency and accuracy of dental caries detection. The objective of this scoping review was to map the scientific evidence available in the literature on the use of artificial intelligence tools for the detection of dental caries. Methods: This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A literature search was performed in the PubMed, Scopus, and Web of Science (WoS) databases using the search expression "artificial intelligence AND dental caries". Inclusion criteria were articles published in the last five years, in English or Spanish and available in full text. Results: The initial search yielded 617 records. After reviewing titles, 143 articles were selected, and upon removing duplicates, 93 unique entries remained. Following abstract evaluation, 40 articles were chosen for full-text review. Finally, 30 publications were included in this review. Conclusions: The evidence shows that artificial intelligence (AI) tools applied to dental caries detection have demonstrated significant improvements in diagnostic accuracy, with sensitivity and specificity values that are comparable to or even exceed those of traditional methods. Thus, the evidence indicates that the integration of AI is no longer a question of if, but how profoundly it will reshape the existing paradigms of dental diagnostics.