The Application and Performance of Artificial Intelligence (AI) Models in the Diagnosis, Classification, and Prediction of Periodontal Diseases: A Systematic Review

Abstract

<b>Background/Objectives</b>: Artificial intelligence is revolutionizing healthcare across multiple areas, and periodontology is no exception to this emerging trend. This systematic study sought to rigorously assess the applicability and efficacy of artificial intelligence (AI) models in the diagnosis, classification, and prediction of periodontal diseases. <b>Methods</b>: A web-based search was performed across many reputable databases, including PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library. Articles published between January 2000 and January 2025 were included in the search. Following the application of the inclusion criteria, 33 publications were selected for critical analysis utilizing QUADAS-2, and their certainty of evidence was evaluated using the GRADE technique. <b>Results</b>: The primary applications of AI technology include the diagnosis, classification, and grading of periodontal diseases; diagnosis of gingivitis; evaluation of the radiographic alveolar bone level and degree of alveolar bone loss; and prediction of periodontal disease risk. The AI models utilized in these studies outperformed current clinical methods in diagnosing, classifying, and predicting periodontal diseases, demonstrating a superior level of precision and accuracy. Their accuracies ranged from 73% to 99.4%, their sensitivities from 75% to 100%, and their precisions from 56% to 99.5%. <b>Conclusions</b>: AI has a lot of potential to help with periodontal diagnosis and risk assessment. Its performance is often similar to or better than that of traditional clinical approaches. But before it can be used widely in clinical settings, problems with the quality of the dataset, its generalizability, its interpretability, and its acceptance by regulators must be solved. AI should be seen as a tool that helps doctors make better decisions and not as a way to replace their knowledge and skills.

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