Mapping global research on artificial intelligence in physical therapy: a bibliometric analysis from 1990 to 2023
Bibliographic record
Abstract
Background The application of artificial intelligence (AI) in physical therapy has garnered increasing interest in recent years.Objectives We aimed to explore the current state of research on AI applications in physical therapy using bibliometric methods.Methods A comprehensive literature search was conducted in Scopus (1990–2023). Two independent reviewers assessed titles, abstracts, and full documents. Inclusion criteria consisted of documents addressing AI applicability in physical therapy. Bibliometric analysis was conducted using VOSviewer and the R package Bibliometrix.Results A total of 805 studies were retrieved. After applying exclusion criteria and screening, 460 documents published across 317 journals were included, showing an annual growth rate of 16.7%. The average document age was 5.1 years. Contributions came from 1974 authors, with the University of Toronto being the most prolific institution. Research originated from 65 countries, led by the USA, followed by China, India, Germany, and Canada. Key themes included ‘machine learning’, ‘rehabilitation’, ‘physiotherapy’, ‘artificial intelligence’, ‘physical therapy’, and ‘deep learning’.Conclusion The number of publications on AI in physical therapy has grown significantly. Despite this, there is a notable gap in international collaboration, with research primarily centred in high- and upper-middle-income countries. Findings provide valuable insights into underexplored topics representing potential areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.242 | 0.312 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".