Systematic reviews on artificial intelligence applications in dentistry- A bibliometric evaluation

Background Artificial intelligence (AI) applications are transforming modern dental practice, with novel AI-supported applications continuously emerging across all dental specialties. This bibliometric analysis aimed to comprehensively examine AI-based dental applications' evolution, impact, international research patterns, and emerging focus points. Methods The Scopus database was searched using a combination of relevant keywords. The search was limited to journal articles within the "Dentistry" subject area. Eligible studies were systematic reviews in English published from 2010 to 2025. After manual data cleaning, standardization, and pre-processing, clustering algorithms were applied to identify thematic research areas and evaluate annual publication growth, citation metrics, and trend detection. VOS viewer generated visual network analyses of keyword co-occurrences and collaboration patterns among authors and countries. Results A total of 3,138 documents were identified, of which 89 systematic reviews were eligible. These reviews were published across 53 sources, with an annual growth rate of 30.77%, with eligible systematic review publications significantly initiating from 2019 onward and peaking in 2024. India led in publication volume (n = 12), while Saudi Arabia garnered the most citations (n = 411). The Journal of Prosthetic Dentistry emerged as the most prolific source with eight reviews and the highest impact metrics (H-index = 5, 195 citations). Mohammad-Rahimi and Motamedian were the most productive authors (7 and 6 reviews, respectively), while Patil demonstrated the highest citation impact (412 citations). At the forefront of Institutions was Shahid Beheshti University, Iran, with 26 reviews. Keyword analysis identified "artificial intelligence" as the most frequent term (85 occurrences), with clustering analysis revealing strong interconnections between AI, deep learning, and diagnostic applications. The most influential review was Khanagar et al. 2021, accumulating 274 citations at a rate of 54.80 annually. Conclusion The findings confirm the proliferating research landscape of AI applications in dentistry. While research volume has increased substantially, citation-based indicators reaffirm that methodological rigor and clinical relevance remain paramount in determining academic influence.