评估AI算法对临床决策的解释:基于社会科学的方法
IEEE journal of biomedical and health informatics
|April 25, 2024
概括
我们开发了一个新的指标来评估AI解释对临床医生的有用性. 该工具通过评估解释相关性,帮助选择最有用的AI驱动的临床决策支持系统.
科学领域:
- 人工智能的人工智能
- 临床决策支持系统 临床决策支持系统
- 可解释的人工智能 (XAI)
背景情况:
- 可解释的人工智能 (XAI) 为AI模型预测提供了理由.
- 评估人工智能解释包括评估忠实性和有用性.
- 缺乏在临床环境中解释有用性的自动化指标.
研究的目的:
- 为临床医生开发一种用于评估人工智能解释有用性的新型指标.
- 创建一个考虑人类认知和AI解释临床要求的评分方法.
主要方法:
- 开发了XAI解释提供特征重要性值的评分方法.
- 综合社会科学理论和生物医学知识图表,用于实用性评估.
- 通过对ICU中败血症发病预测的案例研究来评估该方法.
主要成果:
- 新指标的得分与临床文献的证据一致.
- 开发的指标表现出适用于评估AI解释有用性的品质.
- 该方法成功地量化了从临床文献中解释的支持.
结论:
- 拟议的指标可以评估和选择在临床环境中有用的AI解释.
- 这种工具对于设计有效的AI驱动的临床决策支持系统至关重要.
- 促进在医疗保健中推进可靠和临床相关的AI.
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