转变紧急分类:一项初步的,基于场景的横截面研究,比较人工智能模型和临床专业知识,以提高准确性
Bratislavske lekarske listy
|November 2, 2024
概括
与谷歌巴德和ChatGPT等人工智能模型相比,医疗保健专业人员表现出更高的紧急分拣准确性. 将人工智能与人类专业知识相结合,提供了一个更有效的分拣系统.
科学领域:
- 紧急医疗 紧急医疗
- 卫生技术评估 卫生技术评估
- 医疗保健中的人工智能
背景情况:
- 紧急分拣系统对于患者护理至关重要,新技术的持续评估.
- 在医疗保健环境中,主观的人类评估和客观的人工智能 (AI) 评估之间存在差异.
研究的目的:
- 为了比较医疗保健专业人员与人工智能模型在模拟紧急场景中的分拣判断准确度.
- 分析人工智能应用在紧急分拣决策中的有效性.
主要方法:
- 用50个虚拟患者场景来评估紧急分拣的性能.
- 医疗保健专业人员和人工智能系统 (谷歌巴德,ChatGPT v.3.5) 使用标准化分拣系统对患者的敏度进行了分类.
- 紧急医疗专家进行了分分析,以评估场景的尖性.
主要成果:
- 医疗保健专业人员取得的正确分级分类 (n=30.7) 比人工智能系统 (n=25.5) 高得多.
- 在低敏度和高敏度场景中,在Google Bard和ChatGPT之间没有观察到显著的性能差异 (p=0.821).
- 这项研究包括86名医疗保健专业人员,主要是护士和急诊医生.
结论:
- 虽然人工智能可以快速处理数据,但人类临床医生目前表现出卓越的分类能力.
- 建议采用混合方法,将人工智能与人类临床判断相结合,以开发更有效的紧急分拣系统.
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