Generative Artificial Intelligence Methodology Reporting in Otolaryngology: A Scoping Review
LLM-focused literature in OHNS, while exploring many potentially fruitful avenues, demonstrates variable completeness in methodological reporting. This severely limits the generalizability of these studies and suggests that best practices could be further disseminated and enforced by researchers and journals.
Authors
- Isaac L. Alter (ORCID: https://orcid.org/0000-0002-0253-2161)
- Katerina Andreadis (ORCID: https://orcid.org/0000-0001-8586-450X)
- Anaïs Rameau (ORCID: https://orcid.org/0000-0003-1543-2634)
- Karly Chan
Institutions
- Cornell University (US)
- New York University (US)
Publication Details
- Journal
- The Laryngoscope
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1002/lary.70165
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65