Deep learning algorithms have shown benefits for pathology in the context of risk stratification of tumors. Although the results are promising, several steps have to be made to confirm clinical utility. In a recent issue of The Journal of Pathology, Colling et al present a perspective manuscript providing a roadmap to routine use of artificial intelligence in histopathology. In this commentary, we aimed to put these key points in the context of recent findings of AI and digital image analysis studies. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

中文翻译：

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下一代病理学：人工智能增强了组织病理学的实践。

在肿瘤的风险分层中，深度学习算法已显示出对病理学的好处。尽管结果令人鼓舞，但必须采取几个步骤来确认临床效用。在最近出版的《病理学杂志》中，Colling等人提出了一种透视手稿，为组织病理学中人工智能的常规使用提供了路线图。在这篇评论中，我们旨在将这些关键点放在AI和数字图像分析研究的最新发现的背景下。©2019英国和爱尔兰病理学会。由John Wiley＆Sons，Ltd.出版