Assessing ChatGPT’s Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study (Preprint)
Open accessPreprint — published as 10.2196/51157 · Paper page
Abstract
BACKGROUND: ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. OBJECTIVE: The aim of this study was to evaluate ChatGPT’s competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. METHODS: We developed a model termed PanelGPT to assess ChatGPT’s competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. RESULTS: By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. CONCLUSIONS: ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.
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Anthony, C. A., Rojas, E. O., Keffala, V., Glass, N. A., Shah, A. S., Miller, B. J., Hogue, M., Willey, M. C., Karam, M., & Marsh, J. L. (2020) · doi:10.2196/preprints.17750 · Cited by 64 · see citing works
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- Authors
- McBee, J. C., Han, D. Y., Liu, L., Ma, L., Adjeroh, D. A., Xu, D., & Hu, G.
- Affiliations
- West Virginia University (US)
- Arizona State University (US)
- Wright State University (US)
- University of Missouri (US)
- Corresponding author
- Hu, Gangqing
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2023-07-23
- Volume
- 10
- Pages
- e51157-e51157
- ISSN
- 2369-3762 (ISSN-L)
- OpenAlex
- W4400903223
- Cited by
- 14
- References
- 1
- Citations by year
- 2026: 7 · 2025: 7
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (P20GM103434, U54GM104942)
- U.S. National Library of Medicine (R01LM013438, R01LM013392)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- ChatGPT, large language models, chatbots, sports rehabilitation, interdisciplinary research, patient education, data privacy and security, chatbot ethics, human-AI collaboration, medical education
- MeSH terms
- Clinical Competence, Humans, Sports, Interdisciplinary Communication
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