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使用公开可用的人工智能系统对输血后不良事件进行分类

Mark K Fung¹, James P AuBuchon², Laura D Stephens³

¹Department of Pathology and Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, Vermont, USA.

|February 24, 2024

概括

生成型人工智能 (AI) 在分类输血反应方面表现出可变的准确性,正确识别了一些,但错过了其他一些. 虽然人工智能可以帮助识别潜在反应,但建议对当前的临床分类保持谨慎.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
输血医学输血医学

背景情况:

对输血反应的准确分类对于患者护理,供体管理和血液监测至关重要.
生成型人工智能 (AI) 提供了在医疗保健机构诊断援助的潜力.

研究的目的:

将生成AI系统的诊断准确性与输血医学 (TM) 专家在分类输血反应方面进行比较.
评估AI的诊断能力,评估严重程度,并使用既定标准确定输血反应的归因性.

主要方法:

一个人工智能系统评估了36个输血反应案例场景.
人工智能反应与专家小组的分类和先前的TM专家评估进行了比较.
人工智能表现与TM专家进行了基准测试,使用TrDDx基于网络的算法来处理具有挑战性的案例.

主要成果:

根据输血反应类别,AI的准确性差异很大,在一些方面表现出色 (例如,TACO,TRALI),但在其他方面失败 (例如,DSTR).
整体AI准确率为48.7%,而TM专家的准确率为72.1% (p=0.005).
人工智能响应经常使用非标准的术语,缺乏严重性和归因性评估.

结论:

生成型人工智能可以帮助临床医生识别错过的输血反应类别.
目前的AI输出需要在临床应用之前仔细考虑和验证输血反应分类.

关键词:

人工智能的人工智能是人工智能.血液监测血液监测血液监测输血反应输血反应

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