The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.

中文翻译：

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迈向用于精确病理学的可解释人工智能


近年来精准医学的快速发展开始挑战诊断病理学，因为它能够以定量、综合和标准化的方式分析组织学图像和越来越大的分子分析数据。人工智能 （AI） 和更准确地说，深度学习技术最近展示了促进复杂数据分析任务的潜力，包括用于疾病分类的临床、组织学和分子数据;组织生物标志物定量;和临床结果预测。这篇综述对 AI 进行了一般性介绍，并描述了最近的发展，重点是诊断病理学及其他领域的应用。我们解释了包括传统 AI 的黑盒特性在内的局限性，并描述了使用所谓的可解释 AI 使机器学习决策更加透明的解决方案。审查的目的是促进对生物医学和 AI 方面的相互理解。为此，除了提供病理学和机器学习相关基础的概述外，我们还提供了经过处理的示例，以便更好地实际理解 AI 可以实现什么以及应该如何实现。