Artificial Intelligence in Dental Diagnosis: Current Applications, Challenges, and a Clinician-Facing Readiness Framework

Background: Artificial intelligence (AI) is increasingly evaluated for dental diagnosis. This narrative review summarises recent evidence on AI-based diagnostic tools in dentistry and the barriers to their clinical adoption. Methods: PubMed, Scopus and Web of Science were searched for English-language articles published between January 2020 and October 2026. Systematic reviews, meta-analyses and key primary studies were selected for methodological rigour and clinical representativeness. The review was written with reference to SANRA. Results: Applications span radiographic interpretation, caries detection, periodontal assessment and oral cancer screening. Pooled estimates were high but heterogeneous: sensitivity 0.94 and specificity 0.96 for periapical radiolucencies (four of 24 studies pooled), sensitivity 0.90 and specificity 0.89 for oral potentially malignant disorders and oral cancer on clinical imaging, and pooled specificity of about 88% for radiographic caries detection. Randomised reader studies on retrospective images showed mixed effects: higher sensitivity for enamel caries but more invasive treatment decisions in one trial, and a modest accuracy gain from fewer false positives, with unchanged sensitivity, in another. Most evidence is retrospective and single-institution, risk of bias is often unclear or high, and prospective external validation is scarce. The DENT-AI Readiness Framework, a conceptual checklist for general dentists, is proposed. Conclusions: AI is a promising decision-support adjunct. Integration is limited by heterogeneity, limited explainability, potential bias, and unresolved liability and privacy issues. Multicentre prospective validation, standardised reporting and clear regulation are needed. DENT-AI is author-derived and unvalidated.