可解释的人工智能在神经病学决策支持中的影响
Grace Y Gombolay1, Andrew Silva2, Mariah Schrum2
1Department of Pediatrics, Division of Neurology, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, USA.
Annals of clinical and translational neurology
|April 6, 2024
概括
可解释的人工智能 (xAI) 对临床医生的影响不同于一般人群,没有一种方法是普遍有效的. 针对医学的个性化决策支持系统需要定制的xAI解决方案.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 人与计算机的交互
背景情况:
- 基于人工智能 (AI) 的决策支持系统 (DSS) 在医学中越来越多地使用,但它们的内部决策过程往往保持不透明.
- 可解释的人工智能 (xAI) 技术旨在为这些人工智能系统提供透明度,但对临床用户的最佳设计尚不清楚.
研究的目的:
- 调查各种xAI技术对临床医生与基于AI的DSS互动的影响.
- 在决策任务中比较xAI对医疗专业人员和普通人群的影响.
主要方法:
- 一项随机盲目的研究将神经科医生与普通人群进行比较,使用AI-DSS与不同的xAI干预措施 (例如决策树,特征重要性,没有解释).
- 评估的主要结果包括测试表现和用户对可解释性,信任和社会能力的看法.
- 二次结果衡量了对DSS建议的遵守,理解和同意.
主要成果:
- 决策树被神经学家认为比一般人群和概率得分更容易解释.
- 增加神经学经验和感知可解释性与性能下降有关.
- 个别的xAI方法没有预测性能;感知可解释性是关键因素.
结论:
- 与普通人群相比,xAI方法对医疗专业人员的影响不同,表明没有一种适合所有人的解决方案.
- 需要进一步以用户为中心的研究来开发针对临床医生量身定制的个性化AI决策支持系统.
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