Large language models’ performances regarding common patient questions about osteoarthritis: A comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Perplexity
Our findings underscore the potential of LLMs, specifically ChatGPT-4.0 and Perplexity, to deliver accurate and thorough responses to OA-related queries. Targeted correction of specific misconceptions to improve the accuracy of LLMs remains crucial.
Authors
- J. F. Qiu
- Michael Tim‐Yun Ong (ORCID: https://orcid.org/0000-0002-4460-9286)
- Patrick Shu‐Hang Yung (ORCID: https://orcid.org/0000-0002-7214-5503)
- Xueyou Zhang (ORCID: https://orcid.org/0000-0002-2072-7683)
- Zuru Liang (ORCID: https://orcid.org/0009-0009-9766-6944)
- Mingming Cao (ORCID: https://orcid.org/0000-0002-4234-1175)
- Qianwen Wang (ORCID: https://orcid.org/0009-0008-5251-9353)
Institutions
- Chinese University of Hong Kong (HK)
- Shanghai University of Sport (CN)
Publication Details
- Journal
- Journal of sport and health science/Journal of Sport and Health Science
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1016/j.jshs.2024.101016
- Citations
- 15
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.80
Funders
- Chinese University of Hong Kong
- Food and Health Bureau
- Health and Medical Research Fund