医疗保健中的智能系统:对可解释的用户界面进行系统调查
João Cálem1, Catarina Moreira2, Joaquim Jorge1
1Instituto Superior Técnico, Universidade de Lisboa, Portugal; INESC-ID, Portugal.
Computers in biology and medicine
|July 27, 2024
概括
人工智能 (AI) 可以改善医疗保健,但医生需要信任. 这项研究调查了医学中的可解释AI (XAI) 接口,以促进AI诊断工具的理解和采用.
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:
- 全球放射学短缺影响了世界一半以上的人口,增加了对AI在医学诊断和治疗方面的需求.
- 医疗专业人员之间的有限信任是人工智能在医疗保健中广泛采用的重要障碍.
- 可解释的人工智能 (XAI) 通过使复杂的人工智能模型更加透明和易于理解来增强信任.
研究的目的:
- 进行第一个关于可解释用户界面 (XUI) 的调查,特别是在医疗领域.
- 分析当前医疗XAI系统中使用的可视化和交互方法.
- 为在医疗保健中开发更可靠,更有效的AI工具提供基础.
主要方法:
- 使用PRISMA方法的系统文献审查.
- 从医疗AI系统中分析了42个可解释的接口.
- 在医疗XAI中开发界面设计属性的分类法.
主要成果:
- 在当前医疗XAI系统中确定了关键的可视化和交互技术.
- 强调了有效的信息传递在解释过程中的关键作用.
- 将研究论文分为五个不同的专题组.
结论:
- 有效的XUI设计对于建立信任和促进AI在医学中的采用至关重要.
- 未来的研究应该集中在可反驳性,医学图像的反事实解释和LLM集成上.
- 这项工作为在临床决策支持中推进可解释AI提供了框架.
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