Artificial Intelligence Applications in Oral and Maxillofacial Surgery: A Bibliometric and Science Mapping Analysis of Global Research Trends (2000-2025)

Aim: To map the scientific landscape of artificial intelligence (AI) applications in oral and maxillofacial surgery (OMFS) over the last 25 years using bibliometric and science-mapping methods. Materials and Methods: Publications related to AI and OMFS were retrieved from the Web of Science Core Collection. A comprehensive search was performed, and the complete set of 2153 records was exported. After restricting the timespan to 2000-2025 and limiting the document type to articles and reviews, 1955 documents were included. Bibliometric indicators were calculated, and science maps were generated in VOSviewer for co-authorship, co-occurrence, citation, and co-citation analyses. Results: The annual number of publications increased markedly after 2015, with a steep rise from 2020 onwards and a peak of 275 publications in 2025. Overall, 1678 articles (87.8%) and 232 reviews (12.2%) were identified, totaling 38648 citations (mean 20.2 citations per document). The most productive journals were the Journal of Craniofacial Surgery, the Journal of Cranio-Maxillofacial Surgery, and the International Journal of Oral and Maxillofacial Surgery. Keyword co-occurrence analysis revealed major thematic clusters related to orthognathic surgery, mandibular reconstruction, computer-assisted surgery, virtual surgical planning, 3D printing, and deep learning. Conclusion: AI research at the interface of OMFS has expanded rapidly between 2000 and 2025, particularly in AI-assisted surgical planning, anatomical segmentation for operative workflows, and computer-assisted surgery. These findings highlight how AI-supported technologies are increasingly being investigated for integration into contemporary clinical workflows in OMFS and provide guidance for clinicians regarding future implementation and validation priorities. In addition to clinical insights, these findings provide an overview of the AI research landscape in OMFS, offering guidance for future algorithm development, validation strategies, and interdisciplinary research directions.