ArticleJournal of rehabilitation medicine2023
Use of artificial intelligence large language models as a clinical tool in rehabilitation medicine: a comparative test case.
Article in Journal of rehabilitation medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review.JAMA · 2025Pooled it
- Generative AI and Large Language Models in Rehabilitation: A Scoping Review.Life (Basel, Switzerland) · 2026Review
- Prompt Architecture as a High-Impact Design Factor in Expert-Rated Clinical Documentation Quality: A Controlled Comparative Study in Inpatient Rehabilitation.Bioengineering (Basel, Switzerland) · 2026Article
- Benchmarking large language models against human experts in rehabilitation medicine: a multidimensional evaluation.Journal of neuroengineering and rehabilitation · 2026Article
- Current Landscape and Future Directions Regarding Generative Large Language Models in Stroke Care: Scoping Review.JMIR medical informatics · 2025Article
- The Role of Artificial Intelligence Large Language Models in Personalized Rehabilitation Programs for Knee Osteoarthritis: An Observational Study.Journal of medical systems · 2025Observational
- Article
- Understanding Large Language Models in Healthcare: A Guide to Clinical Implementation and Interpreting Publications.Cureus · 2025Review
- MetaRehabVerse: The Great Opportunity to Put the Person's Functioning and Participation at the Center of Healthcare.Neurorehabilitation and neural repair · 2025Review
- ChatGPT's Attitude, Knowledge, and Clinical Application in Geriatrics Practice and Education: Exploratory Observational Study.JMIR formative research · 2025Observational
- Evaluating the role of ChatGPT in rehabilitation medicine: a narrative review.Frontiers in digital health · 2025Review
- Artificial intelligence in personalized rehabilitation: current applications and a SWOT analysis.Frontiers in digital health · 2025Review
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
- Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records.Scientific reports · 2024Article
- Article
- GPT-agents based on medical guidelines can improve the responsiveness and explainability of outcomes for traumatic brain injury rehabilitation.Scientific reports · 2024Article
- Using ChatGPT to promote college students' participation in physical activities and its effect on mental health.World journal of psychiatry · 2024Article
- Large Language Models in Neurosurgery.Advances in experimental medicine and biology · 2024Article
- Advancing psychosocial disability and psychosocial rehabilitation research through large language models and computational text mining.Global mental health (Cambridge, England) · 2024Article
- Stroke Recovery Is a Journey: Prediction and Potentials of Motor Recovery after a Stroke from a Practical Perspective.Life (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the potential use of artificial intelligence language models in formulating rehabilitation prescriptions and International Classification of Functioning, Disability and Health (ICF) codes. Design: Comparative study based on a single case report compared to standard answers from a textbook. SUBJECTS: A stroke case from textbook. Methods: Chat Generative Pre-Trained Transformer-4 (ChatGPT-4)was used to generate comprehensive medical and rehabilitation prescription information and ICF codes pertaining to the stroke case. This information was compared with standard answers from textbook, and 2 licensed Physical Medicine and Rehabilitation (PMR) clinicians reviewed the artificial intelligence recommendations for further discussion.
resultsChatGPT-4 effectively formulated rehabilitation prescriptions and ICF codes for a typical stroke case, together with a rationale to support its recommendations. This information was generated in seconds. Compared with standard answers, the large language model generated broader and more general prescriptions in terms of medical problems and management plans, rehabilitation problems and management plans, as well as rehabilitation goals. It also demonstrated the ability to propose specified approaches for each rehabilitation therapy. The language model made an error regarding the ICF category for the stroke case, but no mistakes were identified in the ICF codes assigned. Conclusion: This test case suggests that artificial intelligence language models have potential use in facilitating clinical practice and education in the field of rehabilitation medicine.
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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 OpenQuestion graph.