GPT-4 outperforms junior expert physical therapists in sports medicine rehabilitation: an evaluation of AI response quality and adaptiveness
Journal article, 2026

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 (p < 0.001). For responses aimed at patients, GPT-4 was rated best in 26 (55%) questions. For responses aimed at physical therapists, GPT-4 was rated best in 34 (64%) questions. Performance varied by topic, but GPT-4 consistently provided more expert-adapted and contextually appropriate information. GPT-4's responses were especially superior in areas like pain, osteoarthritis, and anterior cruciate ligament rehabilitation. 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.

physical therapy

clinical competence

artificial intelligence

physiotherapy

physical therapy modalities

Author

Eric Hamrin Senorski

University of Gothenburg

Ramana Piussi

University of Gothenburg

Janina Kaarre

University of Gothenburg

Robert Feldt

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

University of Gothenburg

Kate E. Webster

La Trobe University

Martin Hagglund

Linköping University

Rebecca Hamrin Senorski

University of Gothenburg

Johan Hogberg

University of Gothenburg

Yinan Yu

Chalmers, Computer Science and Engineering (Chalmers), Functional Programming

University of Gothenburg

Kristian Samuelsson

Sahlgrenska University Hospital

FRONTIERS IN REHABILITATION SCIENCES

2673-6861 (eISSN)

Vol. 7 1853016

Subject Categories (SSIF 2025)

Dermatology and Venereal Diseases

Other Medical Biotechnology

Applied Psychology

Other Medical and Health Sciences not elsewhere specified

Physiotherapy

DOI

10.3389/fresc.2026.1853016

PubMed

42344131

More information

Latest update

7/3/2026 9