Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study
The findings support the versatility and efficiency of LLMs for clinical decision support through on-premises compatibility, addressing data privacy. Further validation with diverse datasets is needed to ensure their reliability and applicability across different perioperative settings.
Authors
- Hyung-chul Lee (ORCID: https://orcid.org/0000-0003-2482-8112)
- H.-C. Kim (ORCID: https://orcid.org/0009-0007-4657-5175)
- Jipyeong Lee
- H. Lee
- Luke Kim
- Leerang Lim
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- National Medical Center (KR)
- Monash University (AU)
Publication Details
- Journal
- Korean journal of anesthesiology
- Published
- 2026-02-03
- DOI
- https://doi.org/10.4097/kja.25646
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea Health Industry Development Institute