Artificial intelligence in rehabilitation: comparing OpenAI ChatGPT recommendations to evidence-based meta-analyses
Bibliographic record
Abstract
Background: ChatGPT, short for Chat Generative Pre-Trained Transformer, is a language model developed by OpenAI that is capable of generating human-like text responses to various prompts.In the field of rehabilitation, ChatGPT has the potential to be a valuable resource for patients and clinicians alike.In light of the open, easy access to this tool, its application in rehabilitation should be considered and evaluated.Aims: This study aimed to test the feasibility of using the OpenAI ChatGPT tool to generate clinical recommendations in rehabilitation and compare its findings with recent meta-analyses. Material and methods:The selection of rehabilitation areas was based on a report on the global estimates of the need for rehabilitation, with the top three disorders being musculoskeletal disorders, neurological disorders, and chronic respiratory diseases.The authors evaluated the responses of ChatGPT to clinical questions in these areas against the existing evidence on rehabilitation interventions from systematic reviews and meta-analyses in the Cochrane Library database.Results: The findings of the study indicate that the AI's recommendations align with the current scientific knowledge on rehabilitation interventions, as demonstrated through a comparison with the Cochrane Library database.Conclusions: Using AI language models like ChatGPT can help identify effective rehabilitation methods for common conditions.
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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 | 0.178 | 0.614 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".