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我们应该把儿科急诊分组交给人工智能吗? 聊天GPT 4o和Grok 3的比较

Emre Aygun¹, Aysenur Imdat¹, Nazan Dalgic²

¹Department of Pediatrics, Şişli Hamidiye Etfal Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye.

Frontiers in pediatrics

|March 5, 2026

概括

与护士和Grok 3相比,ChatGPT 4o在儿科紧急诊断中表现出更高的准确性. 虽然人工智能看起来很有前途,但它应该增强而不是取代护理专业知识,特别是对于患有慢性疾病的重症儿童.

科学领域:

紧急医疗紧急医疗
医疗保健中的人工智能
临床分类是临床分类.

背景情况:

儿科急诊室患者数量不断增加,需要快速准确的分组.
大型语言模型 (LLM) 提供了分类的潜力,但面临着解释性挑战.
这项研究将LLM的表现与儿科紧急诊断中的人类临床医生的表现进行比较.

研究的目的:

为了评估和比较ChatGPT 4o和Grok 3对护士和医生的分拣准确性.
评估人工智能模型在识别重症儿科患者方面的表现.
确定人工智能在增强儿科紧急诊断的临床决策中的最佳作用.

主要方法:

对1505名儿科急诊患者进行前性观察研究 (2025年3月至4月).
由护士,儿科专家 (黄金标准),ChatGPT 4o和Grok 3使用ESI标准进行分类.
使用科恩的卡帕分析了评价者之间的一致性;通过科克兰的Q和麦克内马尔的测试进行了对比比较.

主要成果:

聊天GPT 4o实现了76.1%的准确性,超过了护士 (53.1%) 和Grok 3 (47.0%).
聊天GPT 4o表现出与医生的良好一致性 (κ=0.69) 和最低的平均绝对ESI误差 (0.25).
虽然Grok 3对危急病例 (97.7%) 的敏感度很高,但它显示出显著的过度选 (36.3%). 护士对患有慢性疾病的儿童的重症患者的识别能力得到改善.

结论:

聊天GPT 4o为儿科紧急诊断提供了良好的灵敏度和特异性的平衡.
像ChatGPT 4o这样的AI系统可以提高分拣准确度,但应该补充,而不是取代临床判断.
护士的专业知识仍然至关重要,特别是在识别患有慢性疾病的重症儿童时.

关键词:

人工智能的人工智能是人工智能.应急服务的紧急服务.医院医院医院医院医院儿科儿科儿科儿科选分类是为了分类.

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