A Bibliometric Analysis of Artificial Intelligence Research Published in Endodontic Journals - Calderón-Rojas, Benigno Miguel; Córdova-Esparza, Diana-Margarita; Romero-González, Julio-Alejandro; Gutiérrez-Alvarez, Oscar Alejandro; Durand-Herrera, Daniel (Iranian Endodontic Journal, 2026)
The aim was to identify and analyse publication trends, bibliometric indicators, and research characteristics of artificial intelligence studies published in Endodontic journals. An advanced search was performed in “Web of Science All Databases” and “Scopus” employing the keywords “Endodontics”, “Endodontic”, “Artificial Intelligence”, and “AI”. Articles published in journals containing the terms “Endodontic”, “Endodontics”, or “Endodontology” were included. A total of 214 authors contributed to the 48 analyzed studies. Mohammad‑Rahimi, H., and Nosrat, A., were the most productive authors. The United States contributed the highest number of publications. The Journal of Endodontics was the primary outlet. Publications increased sharply between 2024 and 2025. Basic research predominated, with radiology as the dominant study field. Deep learning was the most frequently used AI methodology, particularly convolutional neural networks for radiographic diagnosis and segmentation. AI research in endodontics has expanded markedly in recent years, driven predominantly by advances in deep learning and imaging analysis.