Artificial Intelligence in Dentistry: A Comprehensive Review of Diagnostic, Predictive, and Personalized Treatment Applications
DOI:
https://doi.org/10.64149/J.Carcinog.24.6s.614-618Keywords:
Artificial intelligence, Dentistry, Diagnostic accuracy, Predictive modeling, Personalized treatment, Deep learning, Clinical decision supportAbstract
Aim: This study aimed to comprehensively review the applications of artificial intelligence (AI) in dentistry, focusing on diagnostic, predictive, and personalized treatment approaches to assess performance, trends, and clinical implications.
Methodology: A systematic search of PubMed, Scopus, Web of Science, and Google Scholar (2015–2025) identified 125 relevant studies, including original research, reviews, clinical trials, and in vitro/in silico investigations. Data on AI applications, performance metrics, methodologies, and clinical outcomes were extracted and analyzed.
Results: AI was predominantly applied in diagnostics (68%), followed by predictive modeling (20%) and personalized treatment (12%). Diagnostic systems demonstrated high sensitivity (88–96%) and specificity (85–94%), predictive models showed 82–91% accuracy, and personalized applications improved planning efficiency while reducing clinician workload.
Conclusion: AI demonstrates substantial potential to enhance diagnostic accuracy, support predictive analytics, and enable personalized treatment in dentistry. Its integration can improve workflow efficiency, standardize care, and serve as a reliable adjunct to clinical decision-making.
