Evidence map›Paper›PMID 42344131›Full record

ArticleFrontiers in rehabilitation sciences2026

GPT-4 outperforms junior expert physical therapists in sports medicine rehabilitation: an evaluation of AI response quality and adaptiveness.

Eric Hamrin Senorski, Ramana Piussi, Janina Kaarre, Robert Feldt, Kate E Webster, Martin Hägglund, Rebecca Hamrin Senorski, Johan Högberg, Yinan Yu, Kristian Samuelsson

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In one paragraph

Article in Frontiers in rehabilitation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Eric Hamrin SenorskiUnit of Physiotherapy, Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Ramana PiussiUnit of Physiotherapy, Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Janina KaarreDepartment of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Robert FeldtDepartment of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
Kate E WebsterSchool of Allied Health, Human Services and Sport, La Trobe University, Melbourne, Australia.
Martin HägglundDepartment of Health, Medicine and Caring Sciences, Sport Without Injury ProgrammE (SWIPE), Linköping University, Linköping, Sweden.
Rebecca Hamrin SenorskiUnit of Physiotherapy, Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Johan HögbergUnit of Physiotherapy, Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Yinan YuDepartment of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
Kristian SamuelssonSahlgrenska Sports Medicine Center, Gothenburg, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT, has demonstrated potential in healthcare applications, but its effectiveness in clinical rehabilitation contexts remains underexplored. This study investigated whether GPT-4 (accessed through its online interface ChatGPT) can deliver high-quality, adaptive responses in sports physical therapy comparable to or surpassing those of human professionals. Methods: Fifty-three sports physical therapy questions were developed by senior experts and answered by GPT-4 and three junior expert physical therapists (JEPs). Responses were tailored for different target audiences: patients, physical therapists, and expert physical therapists. GPT-4 was prompted using structured engineering techniques. A blinded panel of three senior physical therapists/researchers assessed responses for quality and adaptiveness, and identified which responses were superior. Results: Across all target audiences, GPT-4 outperformed JEPs in both quality and adaptiveness of responses ( Conclusion: This study demonstrated that GPT-4 is capable to generate high-quality, adaptive responses in the field of orthopedic sports physical therapy, which can surpass the performance of JEPs in a controlled setting.

Indexed as

artificial intelligenceclinical competencephysical therapyphysical therapy modalitiesphysiotherapy

Identifiers

PMID42344131
PMCPMC13288665

What OpenQuestion holds

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Registered trials

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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.