Comparative feasibility of reasoning and non-reasoning large language models for gynecologic cancer emergency care
Both GPT-4o and o3-mini-high are feasible tools for emergency care of patients with gynecologic cancer. GPT-4o may provide advantages, reflecting the pattern-based structure of emergency care in this domain. LLM selection should be based on the domain-specific medical knowledge required rather than on the reasoning status or model version. Further prospective multicenter studies are warranted to confirm our findings as well as clinical effectiveness of LLMs in gynecologic cancer emergency care.
Authors
- Myong Cheol Lim (ORCID: https://orcid.org/0000-0001-8964-7158)
- Sang‐Soo Seo (ORCID: https://orcid.org/0000-0003-2381-0965)
- Jun Hwan Kim (ORCID: https://orcid.org/0000-0002-1162-8616)
- Myong Kim (ORCID: https://orcid.org/0000-0001-6995-6850)
- Sokbom Kang (ORCID: https://orcid.org/0000-0002-8428-9555)
- Juwon Lim (ORCID: https://orcid.org/0000-0002-3206-3757)
- Uisuk Kim (ORCID: https://orcid.org/0000-0002-1279-3701)
- Sang‐Yoon Park (ORCID: https://orcid.org/0000-0001-7624-3373)
- Ji Hyun Kim (ORCID: https://orcid.org/0000-0002-9653-7261)
- J. M. Ham
- Jae Kyung Bae
Institutions
- Seoul National University Hospital (KR)
- National Cancer Center (US)
- National Cancer Center (KR)
Publication Details
- Journal
- BMC Health Services Research
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1186/s12913-025-13668-8
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00