Large Language Models and Otolaryngology
LLMs hold significant promise to enhance biomedical research and patient care. Otolaryngology, with its rich clinical, functional, and multimodal data, is well positioned to benefit from these tools. However, progress requires moving beyond feasibility studies toward clinical trial-like validation and implementation research. Future efforts should prioritize open-source model development, domain-specific fine-tuning, secure multi-institutional deployment, and legal oversight to ensure transparency, reproducibility, and generalizability. By aligning with advancements in other specialties, otolaryngology can leverage its unique resources to pioneer responsible LLM applications that improve efficiency, augment decision support, and transform patient outcomes.
Authors
- Cole Pavelchek (ORCID: https://orcid.org/0000-0001-9249-6637)
- Mona Jawad
- Inez Y. Oh (ORCID: https://orcid.org/0000-0002-0780-8710)
- Matthew Shew (ORCID: https://orcid.org/0000-0002-7971-377X)
- James W. Bao (ORCID: https://orcid.org/0000-0002-0316-3628)
- Aditi Gupta
- William J. B. Powell
Institutions
- Oregon Health & Science University (US)
- Washington University in St. Louis (US)
- Digital China Health (China) (CN)
Publication Details
- Journal
- JAMA Otolaryngology–Head & Neck Surgery
- Published
- 2026-02-05
- DOI
- https://doi.org/10.1001/jamaoto.2025.5335
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00