Comparative feasibility of reasoning and non-reasoning large language models for gynecologic cancer emergency care.

ABSTRACT:

Background

Recent advancements in large language models (LLMs) have led to increasing exploration of their clinical applications, particularly in enhancing decision-making and patient management. This study aimed to evaluate the feasibility of LLMs, specifically the non-reasoning Generative Pretrained Transformer (GPT)-4o and reasoning o3-mini-high, in supporting emergency care of patients with gynecologic cancer.

Methods

In this retrospective, single-center study, 15 real-world emergency cases in gynecologic oncology were selected. Two gynecologic oncology fellows, two obstetrics and gynecology residents, GPT-4o, and o3-mini-high assessed each case through four steps: providing differential diagnoses and suggesting relevant examinations; interpreting findings, establishing

SUBMITTER: Kim J 

PROVIDER: S-EPMC12642088 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature