Bibliometric analysis reveals rising trends and methodologies in AI research for endodontics, suggesting a shift in diagnostic approaches.
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.
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Calderón-Rojas et al. (2026) studied this question.
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