Abstract
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.