Evaluation of the use of artificial intelligence in evidence-based endodontology: Bibliometric and scientometric analysis.

Background: Artificial intelligence (AI) systems have the potential to revolutionize the fields of medicine and dentistry by identifying solutions for managing multiple clinical problems. This greatly facilitates the tasks of physicians. Bibliometric studies not only provide insight into the history of a particular topic, but also help to determine how the work evolves over time, and to identify interesting new research.

Objectives: The aim of the present study was to identify and analyze bibliographically recent research articles on the use of AI in endodontics.

Material and methods: The search was conducted in March 2024 in the Web of Science Core Collection (WoS-CC), using the Clarivate™ search engine. The search strategy in all fields included in the database was as follows: "endodontics" was the main keyword, and the other keywords were "artificial intelligence", "deep learning", "machine learning", "artificial neural network", and "convolutional neural network". The title, authors, institution, country, impact factor, total number of citations, year of publication, journal name, number of authors, keywords, abstracts, and other topics of interest were recorded. Bibliometric networks were generated and analyzed using the Visualization of Similarities Viewer (VOSviewer).

Results: Of the 54 articles published by the journals indexed in the WoS-CC between 2012 and 2024 that contained the search terms, 40 were included in this study. The article citations ranged from 0 to168, with an average of 18.97. The number of countries contributing to the research was 29. The country with the highest contribution rate in the field was the USA ranked first (27.5 %), followed by Germany (17.5 %), China (15.0%), and India (15.0%).

Conclusions: Based on this review, it can be concluded that a more significant research interest in AI and endodontics was observed in the USA. The most cited research articles dealt with dental image diagnosis with the use of convolutional neural networks (CNN), the radiologic diagnosis of apical lesions using AI, and the computer-aided diagnosis of periapical lesions using AI in computed tomography (CT) analyses.