AI meets sleep surgery: assessing drug-induced sleep endoscopy interpretation with a large language model
In this single-center study of 16 patients, a large language model accurately interpreted DISE videos and generated safe recommendations consistent with accepted clinical practice, approximating expert performance in most domains. Larger multicenter cohorts are needed to validate these findings and confirm generalizability.
Authors
- Shai Shemesh (ORCID: https://orcid.org/0000-0003-2344-8740)
- Gabriel Nakache (ORCID: https://orcid.org/0000-0002-6783-2165)
- Shibli Alsleibi
- Sholem Hack (ORCID: https://orcid.org/0009-0001-0651-6994)
Institutions
- Tel Aviv University (IL)
- University of Nicosia (CY)
- Sheba Medical Center (IL)
- Wolfson Medical Center (IL)
Publication Details
- Journal
- SLEEP
- Published
- 2025-10-24
- DOI
- https://doi.org/10.1093/sleep/zsaf338
- Citations
- 6
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 6.99