This study aims to map and evaluate research trends in artificial intelligence applied to endodontics using bibliometric methods. A comprehensive literature search was conducted in PubMed, EMBASE, and Scopus in April 2025 to identify studies addressing artificial intelligence in endodontic diagnosis, treatment, and education. Titles, abstracts, and full texts were screened according to predefined criteria. Bibliometric indicators including publication output, authorship networks, institutional and country contributions, citation patterns, and keyword co-occurrence were analysed using bibliometric software. 220 studies were included. Research output increased markedly over time, with major contributions from China, the United States, Turkey and India. Collaboration was largely geographically clustered, with limited international integration. Recent publications showed a strong focus on deep learning approaches for diagnostic imaging, particularly cone-beam computed tomography and image segmentation. Artificial intelligence research in endodontics is expanding rapidly, but broader collaboration and diversification beyond radiographic applications are needed to support clinical translation.
Chen et al. (Tue,) studied this question.
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