Abstract Background: Artificial Intelligence (AI) and Machine Learning (ML) are reshaping modern dentistry by enhancing diagnostic precision, optimizing treatment planning, and improving patient management. These data-driven systems can analyze vast clinical datasets, uncover hidden patterns, and provide evidence-based clinical support. Objective: To systematically evaluate and analyze the applications, diagnostic accuracy, and outcome measures of AI and ML tools across various dental specialties, and to synthesize statistical evidence supporting their clinical performance. Methods: Following the PRISMA 2020 guidelines, a comprehensive search was performed across PubMed, Scopus, Web of Science, and Google Scholar for studies published between January 2019 and December 2024. Inclusion criteria comprised original English-language studies employing AI or ML for diagnostic, predictive, or prognostic purposes in dentistry. The QUADAS-2 tool was applied for quality and bias assessment. Pooled sensitivity, specificity, and accuracy values were computed for AI-based diagnostic models using a weighted mean analysis. Results: Of 342 studies initially identified, 39 met inclusion criteria. The pooled diagnostic accuracy of AI models was 92.4% (95% CI: 88.6–96.3%), sensitivity 90.1% (95% CI: 86.4–94.2%), and specificity 91.7% (95% CI: 87.5–95.6%). CNNs, ResNet, and VGG-based architectures were the most frequently used (56% of studies). Applications spanned caries detection (mean AUC = 0.93), oral cancer identification (mean accuracy = 97.8%), periodontal bone loss analysis (AUC = 0.89), cephalometric landmark identification (AUC = 0.96), and implant planning (precision = 0.94).Meta-analytic synthesis revealed that AI models outperformed human experts in 68% of comparative studies, with statistically significant improvements (p < 0.05) in diagnostic sensitivity for image-based tasks. Conclusion: AI and ML demonstrate high diagnostic validity and reliability in dentistry, showing strong evidence of outperforming traditional assessment in early disease detection, classification, and treatment planning. However, standardized validation protocols, multi-center datasets, and explainable algorithms are crucial for clinical translation. AI should be regarded as an adjunct to—rather than a replacement for—clinical expertise, enabling precision-based, patient-centered dental care.
International Journal of Medical Science and Advanced Clinical Research (IJMACR) (Thu,) studied this question.