眼睛跟踪洞察医生的行为,并提供安全和不安全的可解释AI建议
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
|August 2, 2024
概括
可解释的人工智能 (XAI) 并没有提高医生在高风险的临床环境中对不安全的人工智能建议的注意力. 医生对解释的关注与自我报告的有用性没有相关性,突出了当前XAI评估方法的局限性.
科学领域:
- 医疗信息学 医疗信息学
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 可解释AI (XAI) 理论上是为了帮助医生在高风险的临床决策中,通过为AI建议提供上下文.
- 对XAI对医生行为影响的客观评估至关重要,因为自我报告可能无法捕捉到真正的交互动态.
研究的目的:
- 研究医生在模拟的临床环境中对XAI做出安全与不安全的AI建议时的反应.
- 用神经行为方法客观地测量不同XAI类型对医生的注意力和决策的影响.
主要方法:
- 在临床模拟套件中,利用眼睛跟踪记录了19名ICU医生的明显视觉注意力.
- 评估了与四种XAI类型配对的安全或不安全的AI建议之前和之后的处方决策和注意力模式.
主要成果:
- 不安全的AI建议比安全的AI建议引起了医生的更多关注.
- 在不安全的AI场景中,没有特定的XAI类型明显增加了注意力,这表明XAI没有"拯救"决策.
- 医生自我报告的解释的有用性与他们的实际注意力水平没有相关性.
结论:
- 当前的XAI方法可能无法有效地减轻在重症监护机构中与不安全的AI建议相关的风险.
- 像眼睛跟踪这样的客观措施对于全面了解人与人工智能交互至关重要,它补充了主观的自我报告.
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