Year: 2025
Type: article
Abstract: Purpose: The aim of this research is to examine the application of AI-based diagnostic systems for early dental caries and periodontal disease detection and their benefits compared to conventional diagnosis. Methodology: A narrative review of peer-reviewed literature published between 2020 and 2024 on studies using AI models such as CNNs and YOLO in dental diagnosis was done. Diagnostic accuracy, sensitivity, and specificity were contrasted across studies chosen. Results: AI models exhibited excellent performance with caries detection sensitivities of 0.88–0.96 and periodontal disease detection accuracy of up to 94.4%, in many cases, equalling or exceeding clinician performance. Conclusion: AI-aided diagnostics greatly enhance the early detection of dental diseases, although issues such as data privacy and clinical integration need to be resolved for wide-scale adoption.
DOI: 10.62019/d16rqp43
Author: Abdullah Saleem
Institution: University of Ulster
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4410094681