Artificial Intelligence Applications in Periodontology: A Bibliometric Analysis

Background and Aim: To provide the first bibliometric analysisfocused exclusively on artificial intelligence (AI) in periodontology,mapping publication trends, contributors, collaboration networks,and thematic hotspots.Materials and Methods: The Web of Science Core Collectionwas searched on May 8, 2025 using AI- and periodontologyrelatedterms. Only original research articles were included.Bibliographic data from 206 eligible records were analyzed inVOSviewer (v1.6.20) to generate networks of co-authorship,keyword co-occurrence, country- and institution-levelcollaboration, citation patterns, and source journals; minimumthresholds were applied for robust clustering.Results: Relevant literature began in 2005, with a sharp riseafter 2018 and a peak in 2024. The United States and Chinawere the most productive countries, and Türkiye ranked third.In the co-authorship network, Kaan Orhan occupied the mostcentral position. Dominant keywords were “periodontitis,”“machine learning,” and “deep learning,” indicating an emphasison diagnostic/classification tasks. Temporal overlays showed ashift from early algorithm development toward clinically orienteddiagnostic and decision-support applications. Citation mappingsuggested that earlier influential papers were often isolated,whereas recent studies were more integrated. The Journal ofDentistry was the most productive and well-connected source.Conclusion: AI research in periodontology is expanding rapidlywith increasing clinical orientation. Strengthening internationalcollaboration and extending work beyond diagnosis totherapeutic and prognostic applications could enhance clinicalimpact and global integration.