Utilization of ChatGPT for Rhinology Patient Education: Limitations in a Surgical Sub‐Specialty
This preliminary study demonstrates overall near-complete and variably accurate responses provided by ChatGPT to common rhinologic questions, demonstrating important limitations in nuanced subspecialty fields.
Authors
- Katie M. Phillips (ORCID: https://orcid.org/0000-0001-6304-3508)
- Ashoke R. Khanwalkar (ORCID: https://orcid.org/0000-0001-9960-7758)
- Alice E. Huang (ORCID: https://orcid.org/0000-0002-8326-4997)
- Jayakar V. Nayak (ORCID: https://orcid.org/0000-0003-4454-0861)
- Carol H. Yan (ORCID: https://orcid.org/0000-0003-1934-4922)
- Peter H. Hwang (ORCID: https://orcid.org/0000-0002-0786-4675)
- Michael Yong (ORCID: https://orcid.org/0000-0002-7774-5786)
- Michael T. Chang (ORCID: https://orcid.org/0000-0001-9519-5880)
- Zara M. Patel (ORCID: https://orcid.org/0000-0003-2072-982X)
Institutions
- University of California San Diego (US)
- University of Cincinnati Medical Center (US)
- University of Colorado Denver (US)
- Stanford University (US)
Publication Details
- Journal
- OTO Open
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1002/oto2.70065
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31