Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems
Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems
复制标题
人工智能解释对人类对面向患者的人工智能驱动的医疗系统的看法的影响
DOI：
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发表时间：
2021
影响因子：
5.3
通讯作者：
Xiangmin Fan
中科院分区：
文献类型：
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作者：
Zhan Zhang;Y. Genc;Dakuo Wang;M. Ahsen;Xiangmin Fan
正在进行的研究工作一直在研究如何利用人工智能技术来帮助医疗保健消费者理解他们的临床数据，例如诊断放射学报告。如何促进这种新技术的接受是一个热门的研究课题。最近的研究强调了提供有关AI预测和模型性能的本地解释的重要性，以帮助用户确定是否信任AI的预测。尽管做出了一些努力，但已经进行了有限的实证研究，以定量衡量人工智能解释如何影响医疗保健消费者对使用面向患者的人工智能医疗保健系统的看法。本研究的目的是评估不同的AI解释对人们对AI驱动的医疗保健系统的看法的影响。在这项工作中，我们设计并部署了一个大规模的实验（N = 3，423）亚马逊土耳其机械（MTurk），以评估人工智能解释对人们理解放射学报告的感知的影响。我们根据两个因素创建了四个组-预测的解释程度（高与低透明度）和模型性能（好与弱AI模型）-并将参与者随机分配到四个条件之一。参与者被指示将放射学报告分类为描述正常或异常发现，然后完成研究后调查，以表明他们对AI工具的看法。我们发现，揭示模型的性能信息可以促进人们的信任和感知有用的系统输出，而提供本地解释的预测的理由可以促进可理解性，但不一定信任。我们还发现，当模型性能较低时，AI系统披露的信息越多，人们就越不信任该系统。最后，人类是否同意AI预测以及AI预测是否正确也会影响AI解释的效果。最后，我们讨论了为医疗保健消费者设计人工智能系统来解释诊断报告的意义。
Ongoing research efforts have been examining how to utilize artificial intelligence technology to help healthcare consumers make sense of their clinical data, such as diagnostic radiology reports. How to promote the acceptance of such novel technology is a heated research topic. Recent studies highlight the importance of providing local explanations about AI prediction and model performance to help users determine whether to trust AI’s predictions. Despite some efforts, limited empirical research has been conducted to quantitatively measure how AI explanations impact healthcare consumers’ perceptions of using patient-facing, AI-powered healthcare systems. The aim of this study is to evaluate the effects of different AI explanations on people's perceptions of AI-powered healthcare system. In this work, we designed and deployed a large-scale experiment (N = 3,423) on Amazon Mechanical Turk (MTurk) to evaluate the effects of AI explanations on people's perceptions in the context of comprehending radiology reports. We created four groups based on two factors—the extent of explanations for the prediction (High vs. Low Transparency) and the model performance (Good vs. Weak AI Model)—and randomly assigned participants to one of the four conditions. Participants were instructed to classify a radiology report as describing a normal or abnormal finding, followed by completing a post-study survey to indicate their perceptions of the AI tool. We found that revealing model performance information can promote people's trust and perceived usefulness of system outputs, while providing local explanations for the rationale of a prediction can promote understandability but not necessarily trust. We also found that when model performance is low, the more information the AI system discloses, the less people would trust the system. Lastly, whether human agrees with AI predictions or not and whether the AI prediction is correct or not could also influence the effect of AI explanations. We conclude this paper by discussing implications for designing AI systems for healthcare consumers to interpret diagnostic report.
DOI：
10.1148/rg.2017160130
发表时间：
2017-03
期刊：
Radiographics : a review publication of the Radiological Society of North America, Inc
影响因子：
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作者：
Erickson BJ;Korfiatis P;Akkus Z;Kline TL
通讯作者：
Kline TL