评估GPT-4o复杂呼吸道病例的紧急处置与肺部咨询:一个诊断准确性研究
Cem Yıldırım1, Ahmet Aykut2, Ertuğ Günsoy2
1Department of Emergency Medicine, Van Education and Research Hospital, Van, Türkiye. yildirimcem06@gmail.com.
像GPT-4o这样的大型语言模型在预测急诊室患者态度方面表现有前途,但在准确识别出院患者方面存在困难. 在临床使用之前需要进一步验证.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 紧急医疗研究 紧急医疗研究
背景情况:
- 大型语言模型 (LLM) 正在探索应急医学的临床决策支持.
- 在预测急诊室 (ED) 处置方面,LLM在现实世界的表现尚未得到充分研究.
- 这项研究评估了GPT-4o对复杂呼吸道病例的处置预测的准确性.
研究的目的:
- 为了评估GPT-4o的诊断准确性,预测ED处置 (出院,病房或ICU入院).
- 评估GPT-4o在三个输入模型中的性能,数据复杂度不断增加.
- 确定GPT-4o对高急性呼吸系统患者处置决策的有用性.
主要方法:
- 在第三级ED中进行的回顾性观察研究 (2024年11月至2025年2月).
- 包括具有复杂呼吸系统表现的ED患者,需要肺科咨询和胸部CT.
- GPT-4o预测了使用不同数据输入模型的配置:基本参数,实验室数据和CT发现.
主要成果:
- 模型3 (大多数数据) 显示了高灵敏度 (91.9%) 的入院,但低的特异性 (20.8%).
- 模型3对排放具有高特异性 (91.9%),但敏感性低 (20.8%).
- 模型1的表现与更复杂的模型相比,在模型中没有观察到统计学上显著的改进.
结论:
- GPT-4o对于识别需要入院的患者,包括ICU病例,具有很高的灵敏度.
- 排放预测的低灵敏度导致过度选,限制了自主使用.
- 概念验证表明GPT-4o可以分层配置;由于局限性 (选择性队列,缺少生命体) 需要前性验证.
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