Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications

Abstract

Purpose: This study employs bibliometric analysis to evaluate research trends, key contributors, and applications of artificial intelligence (AI) models in orthodontic imaging. It highlights the impact and evolution of AI in this field from 1991 to 2024. Material and Methods: A total of 130 documents were extracted from the Scopus database, spanning 33 years of research. The analysis examined annual growth rates, citation metrics, AI model adoption, and international collaborations. Network visualization was performed using VOSviewer to map research trends and co-authorship networks. Results: The study analyzed 96 publications from 47 sources, revealing exponential growth in AI research-particularly after 2010, with a peak in 2023. The findings show a steady annual growth rate of 9.66% and a maximum citation count of 138 for an AI-based cephalometric analysis study. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) dominate AI applications in orthodontic image analysis. An h-index of 23 and a g-index of 38 reflect the field's significant research impact. Strong international collaborations were observed, with 28.12% of studies involving cross-border research. Conclusion: This analysis highlights the growing influence of AI in orthodontic imaging and emphasizes the need for larger datasets, improved model interpretability, and seamless clinical integration. Addressing these challenges will further enhance AI-driven diagnostics and treatment planning, guiding future research and broader clinical applications.

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