Objective: This study presents a comprehensive bibliometric analysis of artificial intelligence (AI) applications in dentistry between 2000-2025, aiming to reveal global publication trends, thematic concentrations, and collaborative networks. Material and Methods: A systematic search was conducted in the Web of Science Core Collection using controlled vocabulary and Boolean operators. A total of 1,445 English-language articles were retrieved. Bibliometric indicators-such as publication growth, authorship patterns, citation impact, keyword clustering, and co-authorship networks-were analyzed using VOSviewer and RStudio. Results: AI-related publications in dentistry have shown a 14.07% annual growth rate, with a marked acceleration post-2019. Contributions originated from 92 countries and over 1,100 institutions, with the United States, China, and Türkiye leading in output. Citation impact was highest among authors from Germany and South Korea. Keyword co-occurrence analysis revealed 4 dominant clusters focused on diagnostic imaging, machine learning, radiology, and algorithm performance. High-impact publications centered around deep learning applications in caries detection and radiographic diagnosis. Conclusion: The findings underscore AI's transformative role in diagnostic and radiological practices in dentistry, with increasing international collaborations and institutional engagement. However, thematic scope remains concentrated, indicating the need for future research in underexplored areas such as prosthodontics, pediatric dentistry, and ethical dimensions of AI. This study provides an evidence-based roadmap for researchers, educators, and policymakers to guide strategic developments and equitable AI integration in dental science.
ÖZBEY et al. (Thu,) studied this question.