Bibliometric and Social Network Analysis of Artificial Intelligence Research in Dentistry: Global Trends, Collaboration Networks, and Implications for Healthcare Management (2018–2025)

Abstract

Purpose of the Study: Artificial intelligence (AI) has expanded rapidly across dentistry over the past decade, yet the field's global intellectual structure, collaboration patterns, and thematic evolution remain unevenly mapped. This study examines the scientific structure, collaboration networks, and thematic evolution of AI research in dentistry from 2018 to 2025 and derives implications for healthcare management amid digital transformation.                                                                                

Method: Bibliographic records were retrieved from the Web of Science Core Collection in  March–April 2026. A Topic-field Boolean query combining AI-related and dental terms yielded 5,961 English-language articles and reviews (2018–2025). Bibliometric indicators were produced through Web of Science analytics, and co-authorship, keyword co-occurrence, co-citation, and bibliographic-coupling networks were built in VOSviewer 1.6.20 using full counting and association-strength normalization.

Findings: Annual output grew from 69 publications in 2018 to 2,234 in 2025—a 32.4-fold expansion—accompanied by 89,218 total citations and an h-index of 108. China, the United States, India, Türkiye, and South Korea were the most productive countries. Eight thematic clusters emerged, anchored by diagnostic imaging, disease detection, treatment planning, methodology, generative-AI applications, dental education, clinical decision support, and ethics and governance. The intellectual base centred on clinical-AI researchers and foundational computer-vision methodologists.

Conclusions: AI research in dentistry has matured from a narrow technical niche into a globally distributed field increasingly intertwined with healthcare digitalization. The findings may inform managerial and policy discussions on AI integration in oral healthcare and provide an evidence base for researchers, healthcare managers, and policymakers, rather than a direct assessment of AI implementation.

Keywords

• Artificial Intelligence
• Dentistry
• Bibliometric Analysis
• Social Network Analysis
• VOSviewer
• Healthcare Management

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