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

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.

Authors

Institutions

Publication Details

Journal
The Saudi Dental Journal
Published
2026-05-01
DOI
https://doi.org/10.1007/s44445-026-00160-0
Primary Topic
Oral microbiology and periodontitis research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Long Cheng, 周武彦, Yaoxu Li, Xun Liu et al.
The Saudi Dental Journal
Oral microbiology and periodontitis research
article

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

Long Cheng, 周武彦, Yaoxu Li, Xun Liu, Jinghong Yu, Fulin Jiang, Guanliang Wang
article en

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.

The Saudi Dental JournalVol. 38(5)
Chongqing Three Gorges Central Hospital (CN), Stomatological Hospital of Chongqing Medical University (CN), First People's Hospital of Chongqing (CN), Chongqing Three Gorges University (CN), Chongqing Medical University (CN)
Openalex Percentile: Top 7%
Oral microbiology and periodontitis research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.