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用人工智能赋予前线医生权力:评估日常耳,耳,耳科护理中的大型语言模型
Sholem Hack1, Habib G Zalzal2, Rebecca Attal1
1City St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center, Ramat Gan, Israel.
The American journal of emergency medicine
|January 24, 2026
概括
大型语言模型 (LLM) 在协助耳鼻喉诊断和管理在初级和急救护理方面表现有前途. 在模拟场景中,LLM表现出与医生可比的准确性,在模拟场景中改善了转诊适当性.
科学领域:
- 医疗人工智能 医疗人工智能
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 正在出现,用于临床决策支持,但它们在初级和急诊护理,特别是耳鼻喉科的使用是有限的.
- 诊断的不确定性和不适当的转诊在耳鼻喉疾病带来风险和效率低下.
研究的目的:
- 将高级LLM的诊断,管理和转诊性能与模拟耳鼻喉病情景中的医生进行比较.
- 评估与初级和急诊护理相关的普通和高急性耳鼻喉疾病的LLM能力.
主要方法:
- 使用了12个经过验证的耳鼻喉科临床图片.
- 一百名医生和四名法学士 (Gemini-2.0,ChatGPT-4.0,ChatGPT-5,OpenEvidence) 完成了这些图片.
- 输出被一个盲目的专家小组通过使用医疗人工智能工具质量分析来评价.
主要成果:
- 医生实现了91.6%的诊断和87.9%的管理准确度.
- LLM的诊断和管理准确性与医生的诊断和管理准确性相当.
- 与医生相比,LLM表现出更高的转诊适当性,而医生在非紧急病例中有30.4%的不适当转诊率.
结论:
- 在模拟耳鼻喉病例中,LLM表现出一致的,符合指导方针的推理.
- 在紧急和初级保健中,LLM有潜力支持,而不是取代临床判断.
- 负责任的整合和现实世界的验证对于LLM在临床实践中的采用至关重要.
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