Artificial Intelligence in Dental Disease Detection: Diagnostic Performance, Validation Gaps, and Pathways to Clinical Translation

Background: Artificial intelligence (AI) is increasingly being investigated for dental disease detection, including dental caries, periodontal bone loss, periapical radiolucencies, and oral mucosal lesions. Although promising diagnostic performance has been reported, the clinical usefulness, external validity, and reliability of these technologies require careful evaluation. Objective: This critical narrative literature review examines the diagnostic performance of AI-based systems in dentistry, identifies methodological limitations in existing studies, and evaluates the requirements for translating artificial intelligence from experimental research into routine clinical practice. Methods: The review critically synthesizes selected peer-reviewed studies, systematic reviews, diagnostic accuracy investigations, and methodological guidelines published up to 1 February 2026. The analysis focuses on clinical diagnostic tasks, imaging modalities, reference standards, validation procedures, sources of bias, and clinical applicability. No original clinical experiments, patient recruitment, or AI model training were performed. Main Findings: AI-based systems demonstrate promising performance in detecting dental caries, periodontal bone loss, periapical abnormalities, and certain oral mucosal lesions. However, reported diagnostic accuracy varies according to dataset characteristics, imaging techniques, reference standards, and validation methods. Important limitations include retrospective study designs, insufficient external validation, data leakage, limited dataset diversity, algorithmic bias, and a lack of prospective clinical outcome studies. Evidence suggests that AI may serve as a valuable diagnostic support tool, but improvements in laboratory performance do not necessarily translate into better clinical decisions or patient outcomes. Conclusion: Artificial intelligence has substantial potential to support dental diagnostics, particularly when integrated into clinician-supervised workflows. Nevertheless, responsible clinical implementation requires robust multicenter validation, transparent reporting, ethical data governance, continuous performance monitoring, and prospective evaluation of patient-relevant outcomes.