可解释的人工智能在医疗保健中的基本特性和解释有效性:系统性审查
Jinsun Jung1,2, Hyungbok Lee1,3, Hyunggu Jung4,5
1College of Nursing, Seoul National University, Seoul, Republic of Korea.
Heliyon
|May 26, 2023
概括
可解释的人工智能 (XAI) 在医疗保健方面表现有前途,但未被用于实时患者护理. 研究需要为XAI解释和评估在不同利益相关者的标准化框架.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能的人工智能
- 可解释的人工智能 (XAI)
背景情况:
- 信息技术的进步刺激了医疗保健中可靠的可解释AI (XAI) 的发展.
- 尽管性能有所改善,但XAI技术尚未被整合到实时患者护理中.
研究的目的:
- 系统地审查研究趋势,并确定医疗保健中的可解释AI (XAI) 中的差距.
- 评估XAI的基本特性,并评估在临床环境中解释的有效性.
主要方法:
- 使用临床数据对XAI模型进行PubMed和Embase数据库 (20112022) 的系统文献搜索.
- 对符合条件的文章进行选和审查,以确定XAI属性和解释有效性的措施.
主要成果:
- 在882篇文章中,有6篇符合资格标准,重点关注人工智能用户作为利益相关者.
- XAI的目的包括评估,证明,改进和学习;解释质量通过忠实性,可解释性和可信性进行评估.
- 用户满意度是解释有效性的首要指标,其次是信任度,可更正性和任务性能.
结论:
- XAI的研究需要一个全面的解释框架.
- 需要标准化的方法来评估XAI解释对各种利益相关者的有效性.
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