The Clinical Efficacy and Diagnostic Accuracy of Artificial Intelligence Across Dental Specialties

A Comprehensive Systematic Review

Abstract

The integration of Artificial Intelligence (AI) into modern dentistry has already transformed diagnostic and treatment planning approaches; yet the impact of AI in day-to-day clinical practice remains disjointed across different disciplines. Although the laboratory findings of numerous controlled algorithmic validation trials are quite impressive, we must ask ourselves if the clinical applications of AI will indeed bring improvements to our patients' clinical outcomes as they are applied within the dynamic and unpredictable real-world conditions of dentistry. This comprehensive systematic review consolidates research assessing the diagnostic accuracy and clinical efficacy of AI throughout all specialties of dentistry - from oral and maxillofacial surgery to pedodontics to ascertain whether AI is currently an innovative new technology or an incremental advancement. The PubMed, Scopus, Web of Science, and IEEE Xplore literature databases were systematically searched for all prospective and retrospective studies on diagnostic accuracy and/or clinical application involving an AI tool versus a established clinical standard of care (from January 2020 to January 2026). Any peer-reviewed publication reporting new data from the use of clinical AI in direct patient care was considered eligible. Measures of diagnostic accuracy (including sensitivity, specificity, and area under the curve - AUC) along with data on AI-guided treatment planning accuracy and any patient-centered outcomes were extracted. Heterogeneity among the studies was assessed by calculating the value, defined as: low heterogeneity (I≤40%), moderate(40%<I<60%), and high(I>60%). Bias in each study was critically assessed using the QUADAS-2 tool, while clinical translation potential was assessed with the ROB-2 tool. The findings indicated considerable disparity in how the validation of AI applications in dentistry was reported. While many deep learning applications achieved impressive results for image-based diagnostics, specifically in radiology (pooled sensitivity=0.94; 95% CI, 0.89-0.97) and oral pathology (pooled AUC >0.90), the real-world clinical applications and surgical uses of AI have not been sufficiently investigated. Very few prospective clinical trials were found, and only 23% of included studies had prospective designs; nearly all studied enterprises exhibited external validity biases and did not detail the process by which AI was integrated into clinical workflows or its actual effects on patient health, leading to the limited analysis of patient-centered variables. Significant challenges exist in transitioning AI developed in laboratory settings into routine chair side clinical application, prompting the necessity for uniform methods to validate AI tools for responsible innovation. An accurate accounting of AI's real-world role in all facets of dentistry would aid clinicians, researchers, and policymakers in understanding AI's true value, informing sensible and evidence-based adoption rather than overzealous enthusiasm.