Decade-long insights into AI for orthopedic rehabilitation mapping research networks and future trajectories

Background Artificial intelligence (AI) has emerged as a transformative force in orthopedic rehabilitation, yet the field lacks a comprehensive bibliometric overview. This study aims to quantify research trends, key contributors, and emerging hotspots in AI applications for orthopedic rehabilitation from 2016 to May 2025. Objective To provide a comprehensive bibliometric analysis of AI applications in orthopedic rehabilitation, identifying research trends, key contributors, and emerging hotspots to guide future research directions. Methods We retrieved 1866 English-language articles and reviews from the Web of Science Core Collection using predefined AI-and-orthopedic rehabilitation search terms. Bibliometric and visualization analyses were performed with CiteSpace and VOSviewer to map collaborations, co-citation relationships, and keyword co-occurrence patterns. Results Annual publication output exhibited exponential growth, with a pronounced increase beginning in 2018. The United States and China dominated research output. Friedrich Alexander University Erlangen–Nuremberg emerged as the top institution, and Bjoern M. Eskofier was the most cited author. Core publication venues included Sensors and IEEE-affiliated journals. Keyword clustering identified four major hotspots: gait analysis, motion capture, feature extraction, and fall risk and recent citation bursts in terms such as “pressure sensor” and “lower extremity.” Conclusions Identified hotspots and emerging trends offer guidance for future investigations, despite limitations related to database and language scope. This bibliometric analysis provides a foundation for deeper AI integration in orthopedic rehabilitation.