医学中的可解释的人工智能:将XAI整合到未来的临床常规中的挑战
Tim Räz1, Aurélie Pahud De Mortanges2, Mauricio Reyes2,3
1Institute of Philosophy, University of Bern, Bern, Switzerland.
Frontiers in radiology
|September 2, 2025
概括
未来的人工智能系统需要为医疗专业人员提供更好的解释能力. 目前可解释的人工智能 (XAI) 过于严格,需要具有背景意识,对话驱动和社会能力的人工智能用于临床实践.
科学领域:
- 医疗人工智能
- 临床决策支持系统
- 人与计算机的交互
背景情况:
- 目前可解释AI (XAI) 方法缺乏有效融入医疗实践所需的灵活性和临床中心性.
- 现有的XAI方法无法充分解决医疗保健专业人员对AI预测的细微需求.
- 医疗领域对透明和可解释的人工智能的需求日益增长,
研究的目的:
- 提出将先进的XAI整合到未来的医疗环境中的概念路线图.
- 概述开发临床医生友好的XAI系统的关键需求.
- 探索人工智能作为临床环境中的协作参与者的挑战和要求.
主要方法:
- 基于一个虚构的中风治疗场景的概念框架开发.
- 确定和定义高级XAI的三个核心需求:上下文/用户依赖性,真正的对话和社会能力.
- 综合医学XAI,临床实践和哲学概念的跨学科方法.
主要成果:
- 确定了医疗XAI的三个不断升级的要求:上下文/用户特定的解释,人工智能-人类对话和人工智能社会智能.
- 使用中风情景在临床工作流程中说明了这些挑战的出现.
- 在医疗人工智能中定义了"真正的对话"和"社会能力"等关键概念.
结论:
- 未来的医疗人工智能需要超出目前XAI能力的复杂解释能力.
- 实现有效的临床人工智能集成需要能够进行上下文意识的交互,真正的对话和社会理解的人工智能系统.
- 拟议的路线图指导未来的XAI系统在复杂的医疗决策中作为合作伙伴的发展.
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