Computer‐aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/or nonimaging patient data and makes assessment of the patient's condition, which can then be used to assist clinicians in their decision‐making process. The recent success of the deep learning technology in machine learning spurs new research and development efforts to improve CAD performance and to develop CAD for many other complex clinical tasks. In this paper, we discuss the potential and challenges in developing CAD tools using deep learning technology or artificial intelligence (AI) in general, the pitfalls and lessons learned from CAD in screening mammography and considerations needed for future implementation of CAD or AI in clinical use. It is hoped that the past experiences and the deep learning technology will lead to successful advancement and lasting growth in this new era of CAD, thereby enabling CAD to deliver intelligent aids to improve health care.

中文翻译：

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深度学习时代的计算机辅助诊断。

在过去的几十年中，计算机辅助诊断（CAD）一直是研究的主要领域。CAD使用机器学习方法来分析成像和/或非成像的患者数据，并对患者的状况进行评估，然后将其用于协助临床医生的决策过程。深度学习技术在机器学习中的最新成功促使人们进行新的研究和开发工作，以提高CAD性能并开发用于许多其他复杂临床任务的CAD。在本文中，我们将讨论使用深度学习技术或人工智能（AI）总体上开发CAD工具的潜力和挑战，在乳腺X线照相术中从CAD中汲取的陷阱和经验教训以及未来在临床应用中实现CAD或AI所需的注意事项。