量化人工智能建议的影响,并对处方决策作出解释
Myura Nagendran1,2,3, Paul Festor1,3,4, Matthieu Komorowski2
1UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK.
NPJ digital medicine
|November 7, 2023
概括
人工智能 (AI) 建议显著影响了医生行为,但简单的AI解释 (XAI) 并没有增加这种影响. 自我报告的XAI的有用性与实际处方变化没有相关性.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 人与计算机的交互
背景情况:
- 医生行为和决策是复杂的,可以受到各种信息来源的影响.
- 人工智能 (AI) 推和可解释AI (XAI) 对临床实践的影响尚不清楚.
- 了解临床医生如何与人工智能和人类生成的信息互动,对于有效融入医疗保健至关重要.
研究的目的:
- 评估额外信息的影响,包括AI建议和同行建议,对医生处方行为的影响.
- 评估可解释AI (XAI) 是否与单独的AI相比,增强了AI建议的影响.
- 研究临床医生对人工智能的态度,经验和人工智能支持决策的影响之间的相关性.
主要方法:
- 一个经过修改的实验对象之间的实验设计,涉及86名重症监护医生在16个试验中.
- 一个多因素的设计有四个臂:基线 (控制),同行临床医生建议,AI建议和XAI建议.
- 医生为患者病例处方连续药物剂量,AI和XAI提供简单的特征重要性解释.
主要成果:
- 额外的信息,特别是人工智能建议,显著影响了医生处方.
- 简单的XAI并没有比单独的AI建议对处方行为产生更大的影响.
- 对人工智能的态度,临床经验和人工智能支持的决策之间没有发现相关性.
- 自我报告的XAI的有用性与其对处方的实际影响没有相关性.
结论:
- 人工智能建议可以影响医生的处方行为,但简单的XAI在这种情况下提供了有限的额外好处.
- 基于简单特征重要性的XAI在临床决策支持中的边际影响可能很小.
- 自我报告的指标可能无法在专家临床环境中评估XAI的有效性.
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