Large Language Models Show Comparable Response Performance but Vary in Readability Regarding Patient Questions on Hip Arthroscopy
This study provides insights into the accuracy and readability of LLM-generated responses to commonly asked questions about hip arthroscopy. As patients increasingly turn to artificial intelligence tools for health information, understanding the quality and potential risks of misinformation becomes essential.
Authors
- James R. Satalich (ORCID: https://orcid.org/0000-0003-3953-0917)
- Jeffrey Mun (ORCID: https://orcid.org/0000-0002-5732-0422)
- Frederick Mun (ORCID: https://orcid.org/0000-0003-0877-5217)
- Srish S. Chenna
- Brandon J. Allen (ORCID: https://orcid.org/0000-0003-3098-9051)
- Stephen M. Gillinov (ORCID: https://orcid.org/0000-0001-5630-4079)
- Richard M. Wilk
- Scott D. Martin (ORCID: https://orcid.org/0000-0003-1263-5014)
- Rachel L. Poutre (ORCID: https://orcid.org/0009-0007-1280-5825)
Institutions
- Yale University (US)
- Cleveland Clinic Lerner College of Medicine (US)
- Massachusetts General Hospital (US)
- Johns Hopkins Hospital (US)
Publication Details
- Journal
- Arthroscopy Sports Medicine and Rehabilitation
- Published
- 2025-07-30
- DOI
- https://doi.org/10.1016/j.asmr.2025.101229
- Primary Topic
- Hip disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00