Empowering front-line physicians with AI: Evaluating large language models in everyday ENT care
Large language models showed consistent, guideline-concordant reasoning in simulated emergency and primary-care otolaryngology cases. Their potential lies in supporting, not replacing, clinical judgment through responsible integration and real-world validation.
Authors
- Ben Gvili
- Shaked Shivatzki
- Amit Wolfovitz (ORCID: https://orcid.org/0000-0001-5714-5452)
- Habib G. Zalzal (ORCID: https://orcid.org/0000-0002-0777-977X)
- Idit Tessler (ORCID: https://orcid.org/0000-0002-3297-7022)
- Sholem Hack (ORCID: https://orcid.org/0009-0001-0651-6994)
- Rebecca Attal
- Lilia Ann Crew
- Armin Farzad
- Talya Frankel
- Noa Rozendorn
Institutions
- Children's National (US)
- St George's, University of London (GB)
- Touro University California (US)
- Sheba Medical Center (IL)
- Clalit Health Services (IL)
Publication Details
- Journal
- The American Journal of Emergency Medicine
- Published
- 2026-01-20
- DOI
- https://doi.org/10.1016/j.ajem.2026.01.029
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 17.70