Introduction: Artificial intelligence (AI) has gained increasing attention in dentistry, particularly in diagnostic imaging, caries detection, and orthodontic prediction. However, pediatric applications remain underexplored, and no bibliometric synthesis has specifically mapped this research field.
Materials and Methods: A bibliometric analysis was performed using Scopus and Web of Science Core Collection (2000–2024). Search terms included “artificial intelligence,” “machine learning,” “deep learning,” and “pediatric dentistry.” Bibliographic data were analyzed using VOSviewer (v.1.6.20) and Microsoft Excel, focusing on co-authorship networks (authors and countries), keyword co-occurrence networks, citation and co-citation analyses, and bibliographic coupling of publications. Results: A total of 92 publications were identified (Scopus: 53; WoS: 39). Annual outputs increased sharply after 2020, peaking in 2024. India, Turkey, and the United States were the most productive countries, while collaborations across regions remained limited. Keyword clustering revealed three thematic areas: (i) AI-driven diagnostics, (ii) caries risk prediction, and (iii) orthodontic growth assessment. Preventive and behavioral domains were notably underrepresented.
Discussion and Conclusion: Research on AI in pediatric dentistry is rapidly growing but remains diagnostically focused. This study provides the first pediatric dentistry-focused bibliometric overview of AI research, explicitly mapping publication trends, thematic concentrations, and underexplored preventive and behavioral domains. Future research should extend beyond diagnostics, strengthen international collaboration, and address ethical considerations to support responsible clinical integration.