Scientometric Analysis of Artificial Intelligence Applications in Orthopedic Surgery: Global Research Trends and Emerging Hotspots

Background: The integration of Artificial Intelligence (AI) into orthopedic surgery has rapidly evolved, providing transformative opportunities to improve surgical precision, decision-making, and patient outcomes. In spite of the increase in related publications, a thorough scientometric analysis in this field has not yet been performed. Therefore, this study aims to systematically explore the global research trajectory, collaborative networks, influential journals and publications, as well as evolving hotspots in the application of AI to orthopedic surgery using a scientometric method. Methods: A comprehensive literature search was conducted in the Web of Science Core Collection (WoSCC) on March 22, 2025 to include all relevant articles and reviews. VOSviewer and CiteSpace were used to visualize international collaborations, core institutions and journals, co-citation networks, keyword clusters, and citation bursts. Results: A total of 991 publications were retrieved, showing an exponential growth since 2018. The United States emerged as the leading contributor, with Harvard University and the Journal of Arthroplasty being the most productive institution and journal, respectively. Influential authors like Schwab JH and Karhade AV have greatly influenced the field. Keyword and co-citation analyses revealed that the application of prediction models, machine and deep learning in orthopedic surgery such as total knee arthroplasty and spine surgery are the knowledge base, while emerging hotspots include intelligent surgical planning, precise prognosis prediction systems, AI-assisted perioperative management and decision support, specialized applications of large language models, algorithm optimization and verification standards. Conclusion: AI applications in orthopedic surgery are rapidly growing interdisciplinary field led by leading U.S. institutions and journals. Future research is expected to concentrate on improving algorithm interpretability, clinical integration, and global accessibility, leading to precision and intelligent orthopedic treatment and management.