ArticleDigital health
Decade-long insights into AI for orthopedic rehabilitation mapping research networks and future trajectories.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.