Mapping global research on artificial intelligence in physical therapy: a bibliometric analysis from 1990 to 2023
Bibliographic record
Abstract
The application of artificial intelligence (AI) in physical therapy has garnered increasing interest in recent years. We aimed to explore the current state of research on AI applications in physical therapy using bibliometric 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. 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’. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.205 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.325 | 0.013 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".