一个新的XAI框架用于使用生成反事实XAI (GCX) 解释AI-ECG
Jong-Hwan Jang1, Yong-Yeon Jo1, Sora Kang1,2
1Medical AI Co., Ltd., 38, Yeongdong-daero 85-gil, Gangnam-gu, Seoul, Republic of Korea.
Scientific reports
|July 2, 2025
概括
生成反事实可解释的人工智能 (XAI) 创建"如果"心电图 (ECG) 场景,以显示AI如何解释数据. 这种方法通过澄清预测影响来提高临床医生的对AI-ECG诊断的信心.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 目前用于ECG的AI可解释性方法通常突出显示分段,而不详细说明它们对预测的影响.
- 了解ECG解释中的AI决策对于临床采用和信任至关重要.
研究的目的:
- 引入一个生成的反事实框架,以提高解释ECG的AI模型的可解释性.
- 为了使临床医生能够通过生成修改预测值的反事实ECG来探索"假如"情景.
主要方法:
- 开发了一个生成的反事实方法来创建合成ECG.
- 生成反事实ECG以说明特定形态和节奏变化对AI预测的影响.
- 根据已知的临床知识验证生成的反事实,包括与不平衡和心房动相关的变化.
主要成果:
- 该框架成功生成了与临床理解一致的反事实ECG.
- 证明了特定的ECG变化 (例如,T波幅,PR间隔) 如何影响AI-ECG预测.
- 展示了可视化ECG增量修改及其对AI预测的影响的能力.
结论:
- 生成反事实XAI提供了一个强大的工具,用于理解AI-ECG解释超出静态归因图.
- 这种方法有可能显著提高临床医生的对AI-ECG诊断系统的信任和信心.
- 为改善AI在心血管诊断中的可解释性和临床可靠性提供了一个有希望的途径.
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