在医学成像中解释性人工智能的现状和未来方向
Shier Nee Saw1, Yet Yen Yan2, Kwan Hoong Ng3
1Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
European journal of radiology
|December 12, 2024
概括
医学成像中的可解释AI (XAI) 面临着挑战,因为AI的"黑子"性质. 目前的XAI方法缺乏临床采用所需的解释性,阻碍了医疗专家的信任和有效使用.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 这是一个很棒的节目,这是一个很棒的节目.
- 黑盒子是一个黑盒子.
- 人工智能的性质阻碍了临床信任和采用.
- 放射科医生和临床医生在图像解释中使用1型 (直观) 和2型 (分析) 决策.
- 可解释AI (XAI) 旨在为医疗保健中的AI预测提供透明的推理.
研究的目的:
- 审查医学图像解释中的基本决策过程.
- 检查当前用于医学成像的可解释AI (XAI) 方法.
- 确定XAI在临床应用中的发展机遇和挑战.
主要方法:
- 在医学成像和XAI技术中对AI的文献综述.
- 在临床图像解释中分析1型和2型认知过程.
- 评估固有和事后可解释性的方法.
- 展示具有解释功能 (例如热图) 的商业AI系统.
主要成果:
- 目前的XAI方法提供了医疗专家难以理解的解释.
- 商业人工智能系统经常使用热图来解释,但这些可能不足.
- 最先进的XAI还没有成熟到广泛的临床实施.
结论:
- 开发更易于解释的XAI需要对医疗专业人员的认知机制有更深入的了解.
- 弥合人工智能解释和临床理解之间的差距对于信任和采用至关重要.
- 需要进一步的研究才能将XAI推向实际的临床应用.
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