A bibliometric analysis of the literature on the use of artificial intelligence in pediatric dentistry

Abstract

Aims: This study aims to conduct a bibliometric analysis of the scientific literature on the use of artificial intelligence (AI) in pediatric dentistry to evaluate publication trends, citation impact, key contributors, and research themes. Methods: A comprehensive literature search was conducted in the Web of Science (WoS) database up to February 8, 2025. The search included AI-related terms combined with pediatric dentistry keywords. A total of 78 relevant articles were identified and analyzed. VOSviewer software was used for bibliometric mapping, including co-authorship, co-citation, and keyword analysis. Results: The number of publications on AI in pediatric dentistry has increased significantly since 2020, peaking in 2024, followed by a decline in 2025. The analysis identified key research topics, including diagnostic imaging, early childhood caries detection, dental age estimation, and orthodontic assessments. Despite the growth in research output, AI applications in pediatric dentistry remain significantly underdeveloped compared to other dental fields. Citation impact was relatively low, with the most referenced article receiving 83 citations. Conclusion: AI is gaining attention in pediatric dentistry; however, its adoption is still in the early stages. Further research is needed to validate AI models, enhance clinical integration, and expand interdisciplinary collaboration. Addressing data limitations and improving real-world applicability will be crucial for AI’s long-term impact on pediatric dental care.

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