<p>Artificial intelligence and deep learning have expanded dental imaging analysis by enabling automated detection, classification, localization, segmentation, and tooth identification in routinely acquired radiographs. This review provides a practice-oriented synthesis that links the clinical question and required output type to what reported performance actually implies for use in dental care. Using a structured, semi-systematic literature search with quantitative eligibility criteria, we synthesize findings across major application areas including odontogenic cysts and tumors, periapical and apical radiolucencies, dental caries, periodontal bone loss assessment and staging, oral cancer screening, multi-condition diagnosis, and automated tooth numbering. Across these tasks, studies consistently report stronger results for clearly visible pathology and well-defined boundaries, while early-stage disease, small lesions, overlapping anatomy, and restoration-related artifacts drive the most important failure modes. We also highlight why cross-study comparisons are often unreliable due to differences in reference standards, class taxonomies, units of analysis, preprocessing and region-of-interest assumptions, and the frequent absence of external validation. Clinically, the most credible near-term role is calibrated decision support for case prioritization and clinician verification, supported by auditable spatial outputs. Future progress is most directly enabled by multi-center validation, severity-aware reporting, and calibration or uncertainty handling that is aligned with workflow use.</p>