信息相关性和互动性对密集型医生的对基于机器学习的细菌病预测系统的信任的影响:模拟研究
Omer Katzburg1, Michael Roimi2, Amit Frenkel3
1Department of Health Policy and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
JMIR human factors
|August 2, 2024
概括
设计具有相关信息和互动性的用户界面可以提高医生对机器学习 (ML) 临床决策支持系统的信心. 然而,界面上的明确ML算法细节,在密集医生中降低了信任.
科学领域:
- 医疗信息学 医疗信息学
- 人与计算机的交互
- 人工智能在医学中的应用
背景情况:
- 机器学习 (ML) 算法正在迅速发展,但往往是"黑子",导致不信任.
- 在关键的医疗领域,由于潜在的致命结果,不愿意信任ML系统很高.
研究的目的:
- 调查用户界面 (UI) 设计元素如何影响密集医师对基于 ML 的临床决策支持系统 (CDSS) 的信任.
主要方法:
- 47名重症监护医生使用基于ML的模拟系统评估了3例细菌病患者.
- 用户界面条件在信息相关性和互动性方面各不相同.
- 通过与ML预测和实验后问卷的一致性来衡量信任.
主要成果:
- 医生对ML预测的同意不受UI条件的影响.
- 在UI中感知到更高的信息相关性和互动性显著增加了信任 (P<.001).
- 在用户界面上明确显示ML算法特征导致信任度下降 (P=.05).
结论:
- 结合信息相关性和互动性的UI设计对于建立密集人员对ML CDSS的信任至关重要.
- 研究结果强调了UI设计在重症监护病房内人类与ML相互作用中的重要性.
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