Abstract Background: Deep learning and machine learning techniques have become increasingly prominent in dental research as part of the broader integration of artificial intelligence–based approaches. In pediatric dentistry, these methods offer particular potential due to age-specific diagnostic challenges and the need for early detection strategies. Despite growing interest, the overall development and structure of this research field have not yet been comprehensively evaluated. Material and Method: A bibliometric analysis was conducted using publications indexed in the Web of Science Core Collection between 2000 and 2026. A topic-based search strategy combining deep learning– and machine learning–related terms with pediatric dentistry–specific keywords was applied. Bibliometric indicators, including annual publication output, leading journals, authors, countries, institutions, and keyword co-occurrence networks, were analyzed using the bibliometrix package in R and the Biblioshiny interface. Result: A total of 162 publications were included. Scientific output remained limited until 2017, followed by a marked increase after 2020, with the highest number of publications recorded in 2024. China, South Korea, and Saudi Arabia were the most productive countries. The literature was primarily concentrated in dental and interdisciplinary journals. Keyword analysis revealed that research predominantly focused on diagnostic and classification-oriented applications, particularly dental caries. Conclusion: The findings demonstrate a recent and rapid expansion of deep learning and machine learning research in pediatric dentistry, highlighting a growing emphasis on diagnostic support and preventive care. This bibliometric overview provides a structured perspective on current research trends and may guide future investigations in this evolving field.

Abstract Background: Deep learning and machine learning techniques have become increasingly prominent in dental research as part of the broader integration of artificial intelligence–based approaches. In pediatric dentistry, these methods offer particular potential due to age-specific diagnostic challenges and the need for early detection strategies. Despite growing interest, the overall development and structure of this research field have not yet been comprehensively evaluated. Material and Method: A bibliometric analysis was conducted using publications indexed in the Web of Science Core Collection between 2000 and 2026. A topic-based search strategy combining deep learning– and machine learning–related terms with pediatric dentistry–specific keywords was applied. Bibliometric indicators, including annual publication output, leading journals, authors, countries, institutions, and keyword co-occurrence networks, were analyzed using the bibliometrix package in R and the Biblioshiny interface. Result: A total of 162 publications were included. Scientific output remained limited until 2017, followed by a marked increase after 2020, with the highest number of publications recorded in 2024. China, South Korea, and Saudi Arabia were the most productive countries. The literature was primarily concentrated in dental and interdisciplinary journals. Keyword analysis revealed that research predominantly focused on diagnostic and classification-oriented applications, particularly dental caries. Conclusion: The findings demonstrate a recent and rapid expansion of deep learning and machine learning research in pediatric dentistry, highlighting a growing emphasis on diagnostic support and preventive care. This bibliometric overview provides a structured perspective on current research trends and may guide future investigations in this evolving field.