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对于精确病理学的可解释的人工智能.

Frederick Klauschen1,2,3,4, Jonas Dippel3,5, Philipp Keyl1

  • 1Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;

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概括
此摘要是机器生成的。

人工智能 (AI) 提供了强大的工具来分析复杂的病理学数据,改善疾病分类和结果预测. 这篇评论探讨了人工智能应用,局限性和可解释人工智能用于诊断病理学的发展.

关键词:
在XAI,XAI就是XAI.深度学习是一种深度学习.可解释的人工智能病理学的病理学

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科学领域:

  • 病理学 病理学 病理学
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 精准医学需要先进的分析方法来分析组织学和分子数据.
  • 诊断病理学在量化和整合性分析大数据集方面面临着挑战.
  • 人工智能 (AI),特别是深度学习,在应对这些挑战方面表现有前途.

研究的目的:

  • 提供对AI及其在诊断病理学中的应用的介绍.
  • 讨论AI在分析复杂的生物医学数据中的潜力和局限性.
  • 通过实践示例,促进生物医学和人工智能领域之间的理解.

主要方法:

  • 审查人工智能和深度学习的最新发展.
  • 专注于人工智能在疾病分类,生物标志物量化和结果预测方面的应用.
  • 解释AI的局限性,包括"黑子"问题,并介绍可解释AI (XAI).

主要成果:

  • 人工智能在促进病理学任务的复杂数据分析方面显示出巨大的潜力.
  • 可解释AI (XAI) 提供解决方案,以提高机器学习决策的透明度.
  • 经过工作的例子说明了AI在病理学方面的能力和最佳实践.

结论:

  • 人工智能,特别是深度学习,正准备通过实现定量和整合数据分析来彻底改变诊断病理学.
  • 通过可解释性来解决AI的"黑子"性质对于临床采用至关重要.
  • 病理学和人工智能专家之间的协作方法对于成功实施至关重要.