Artificial Intelligence in Periodontology and Implantology: A Narrative Review

Abstract

Artificial intelligence (AI) has become an influential tool in periodontology and implantology; enhancing diagnostic & prognostic accuracy, supporting digital workflows, and improving clinical decision-making. Artificial intelligence is reshaping how clinicians assess, plan, and execute treatments. To provide a comprehensive and clinically relevant overview of current AI applications in periodontology and implantology, evaluate their benefits ,limitations, and possible future directions .This narrative review synthesizes findings from 111 studies including; recent human studies, systematic reviews, and clinical trials exploring technology-driven research exploring AI in diagnostic imaging, periodontal risk prediction, digital implant planning, surgical navigation/robotics, regenerative decision-making, and natural language processing tools. AI demonstrates high diagnostic performance in radiographic interpretation, with reported accuracies ranging from approximately 76% to over 90% depending on tooth type, dataset characteristics, and model architecture. Deep learning models have shown performance comparable to experienced clinicians in controlled experimental settings, particularly in retrospective radiographic datasets. Machine-learning risk assessment models offer personalized predictions for disease progression and implant complications contributing positively to patient centered care. In implantology, AI supports CBCT segmentation, implant position optimization, enhances accuracy through navigation and provides robotic assistance. Emerging applications include automated gingival phenotype assessment, outcome prediction for regenerative procedures, and natural language processing to assess patient/clinician notes. Limitations include dataset bias, lack of external validation, inconsistent reporting standards, and ethical concerns related to transparency and clinical accountability. AI is rapidly advancing across periodontal and implant disciplines, showing potential to improve diagnostic consistency and treatment planning. However, current evidence remains heterogeneous and is largely derived from retrospective or preclinical studies, limiting immediate clinical translation. Further prospective, multicenter validation studies are required before routine clinical implementation.