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Updated: Sep 10, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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超越后期解释:通过透明度,可解释性和可解释性,为医疗成像中负责任的AI提供全面框架

Yashbir Singh1, Quincy A Hathaway2, Varekan Keishing1

  • 1Radiology, Mayo Clinic, Rochester, MN 55905, USA.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
概括

像LIME这样的可解释人工智能 (XAI) 方法在医学成像中显示出比SHAP或Grad-CAM更高的准确性. 然而,事后解释缺乏稳定性, 需要在医疗保健中建立一个新的可信的人工智能问责框架.

关键词:
问责制人工智能临床决策可以解释的AI医学成像进行元分析系统审查

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