Artificial Intelligence in Periodontology: From Automated Diagnosis to Prediction and Clinical Decision Support—A Narrative Review

Background/Objectives: We aimed to evaluate the methodological quality, translational limitations, and clinical applicability of current artificial intelligence (AI) applications in periodontology and to propose a framework for validated prediction and decision support. Methods: A structured narrative review based on a targeted, non-systematic literature search was conducted using PubMed and cross-disciplinary sources (January 2015–April 2026). Evidence from primary studies, systematic reviews, and methodological guidance for AI prediction models and clinical decision-support systems was synthesized with a focus on clinical applicability. Results: Current periodontal AI research is dominated by retrospective studies focusing on radiographic phenotyping, where deep learning models demonstrate promising diagnostic performance for detecting and quantifying periodontal bone loss. However, substantial limitations persist, including heterogeneous endpoints, inconsistent reporting, limited external validation, and insufficient calibration assessment. Importantly, there is little evidence that AI-based tools improve clinical decision-making or patient-relevant outcomes. Emerging work on prognostic modeling and multimodal data integration highlights the potential for individualized periodontal risk prediction but remains undervalidated, with limited evidence for clinical implementation. Conclusions: Although AI-based models show promising diagnostic performance, translational progress in periodontology is currently limited by insufficient validation and the lack of evidence for clinical utility. Future research should prioritize clinically actionable prediction models, robust external validation, and prospective evaluation of AI-supported decision-making within real-world periodontal care pathways.