What question did this study set out to answer?

To provide a comprehensive bibliometric analysis of the integration of artificial intelligence in periodontal disease research.

May 9, 2026Open Access

Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007–2025)

Key Points

To provide a comprehensive bibliometric analysis of the integration of artificial intelligence in periodontal disease research.
Conducted bibliometric analysis using publications from the Web of Science Core Collection (2007-2025)
Utilized R (Bibliometrix), VOSviewer, and CiteSpace for data processing and visualization.
Included 496 articles, comprising 437 research papers and 59 reviews, with analysis of authorship and keyword trends.
Annual publication output showed sustained growth, especially after 2021.
China was the leading contributor with 153 articles, followed by the United States.
Keywords like 'periodontitis', 'machine learning', and 'artificial intelligence' emerged as central themes, with 'progression' and 'expression' as new research directions.

Abstract

Periodontal disease is one of the most common diseases in stomatology. With the continuous development of artificial intelligence (AI), its integration with periodontology is rapidly evolving. However, a comprehensive bibliometric analysis mapping this interdisciplinary field is currently lacking. We conducted a bibliometric analysis by retrieving publications related to AI and periodontal disease from the Web of Science Core Collection (WoSCC) for the period January 2007 to July 2025. Data processing and visualization were performed using R (Bibliometrix), VOSviewer, and CiteSpace. A total of 496 relevant articles (437 research papers; 59 reviews) were included. Annual publication output has shown sustained growth, particularly since 2021. China contributed the most publications (153 articles), followed by the United States. Among institutions, Pusan National University, South Korea (32 articles), and Saveetha Institute of Medical and Technical Science, India (31 articles) were the most productive. BMC Oral Health published the highest number of articles (n = 23). The co-authorship network involved 2,604 authors, with Pradeep Kumar Yadalam being the most prolific (15 articles). Co-citation analysis identified Orhan Kaan, Abu Patricia Angela R., and Falk Schwendicke as the most cited authors. Keyword analysis revealed "periodontitis," "machine learning," and "artificial intelligence" as core research foci, while burst detection indicated "progression" and "expression" as emerging thematic directions. This study provides a systematic overview of the research landscape, highlighting evolving trends, key contributors, and knowledge structure in AI applications for periodontal disease. The findings offer valuable insights to help dentists and researchers understand current applications, identify frontiers, and potentially guide the future clinical translation of AI technologies in periodontology.

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