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评估可解释性:医疗成像中可解释的人工智能特征的系统评估框架

Miguel A Lago¹, Ghada Zamzmi¹, Brandon Eich¹

¹Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.

Bioengineering (Basel, Switzerland)

|January 28, 2026

概括

我们开发了一个框架来评估医疗成像中人工智能 (AI) 的解释性. 该系统使用一致性,可信性,准确性和有用性标准来量化解释质量,以获得更好的人工智能辅助诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
可解释的人工智能

背景情况:

人工智能设备中的可解释性功能为内部机制提供了洞察力.
目前对人工智能解释的评估技术缺乏,特别是在医学成像中.
需要强大的方法来评估医疗保健中人工智能生成的解释的质量.

研究的目的:

提出一个全面的框架来评估和报告医疗图像中的可解释AI (XAI) 特性.
建立可量化的标准来评估AI在医疗器械中提供的解释质量.
开发医学成像中XAI方法的得分卡,以配合AI设备.

主要方法:

开发了一个基于四个标准的评估框架:一致性,可信性,忠实性和有用性.
定义一致性为对类似输入的解释的变化.
将可信性定义为解释与基本真相的接近程度,将忠实性定义为与模型机制的一致性,并将实用性定义为对任务执行的影响.

主要成果:

该框架提供了一种定量方法来评估医学成像中的AI解释质量.
为了完整描述和评估XAI方法,开发了一个得分卡.
使用Ablation CAM和 Eigen CAM绘制的热图评估用于乳腺病变检测的案例研究.

结论:

拟议的框架建立了量化医疗人工智能设备解释质量的标准.
这项工作解决了在医学成像中缺乏XAI的评估技术的问题.
开发的得分表和标准有助于对AI在临床应用中的可解释性进行彻底评估.

关键词:

人工智能的人工智能是人工智能.可以解释性的解释性.热图热图热图热图可以解释的解释性.医学成像医学成像透明度透明度透明度

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