ReviewFrontiers in digital health2025
Evaluating the role of ChatGPT in rehabilitation medicine: a narrative review.
Review in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- Generative AI and Large Language Models in Rehabilitation: A Scoping Review.Life (Basel, Switzerland) · 2026Review
- ChatGPT-generated rehabilitation programs in sports physiotherapy: an expert evaluation and a mixed-methods study of clinical applicability.Frontiers in medicine · 2026Article
- Ethical issues in multi-agent AI systems for healthcare: a narrative review.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chat Generative Pretrained Transformer (ChatGPT) has emerged as a sophisticated artificial intelligence (AI) language model in healthcare. This narrative review examines ChatGPT's current applications and limitations in rehabilitation medicine through analysing multiple studies. While demonstrating promising performance in structured tasks and basic medical guidance, significant challenges persist. These include inconsistent performance in complex clinical scenarios, limited regional adaptability, poor reference reliability, and inadequate safety considerations for special populations. Although innovative approaches like multi-agent systems show potential improvements in accuracy and interpretability, concerns regarding clinical responsibility, data security, and ethical implications remain crucial. As ChatGPT continues to evolve, its optimal integration into rehabilitation practice requires careful consideration of these limitations and appropriate professional oversight. This review aims to provide insights for healthcare professionals and policymakers in navigating the implementation of AI assistance in rehabilitation medicine, emphasizing the need for balanced integration while maintaining clinical safety and effectiveness.
Indexed as
Identifiers
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.