Demystification of AI-driven medical image interpretation: past, present and future
Demystification of AI-driven medical image interpretation: past, present and future
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DOI：
10.1007/s00330-018-5674-x
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发表时间：
2019-03-01
影响因子：
5.9
通讯作者：
Gallix, Benoit
中科院分区：
文献类型：
--
作者：
Savadjiev, Peter;Chong, Jaron;Gallix, Benoit
最近大数据的爆炸性增长开启了人工智能(AI)算法在各个技术活动领域的新纪元，包括医学，特别是放射学。然而，人工智能最近在某些旗舰应用程序上的成功在某种程度上掩盖了数十年来在医学图像分析计算技术开发方面的进展。在这篇文章中，我们概述了放射图像分析的人工智能方法的历史，以便为最新的发展提供一个背景。我们回顾了更经典的方法以及更新的深度学习技术的功能、优点和局限性。我们讨论了医学数据和医学科学的独特特征，这些特征使医学有别于其他技术领域，以便不仅突出人工智能在放射学中的潜力，而且还强调可能限制某些人工智能方法适用性的非常真实且经常被忽视的限制。最后，我们对人工智能对放射学的潜在影响以及如何不仅从技术角度而且从临床角度对其进行评估，使患者最终受益于它提供了一个全面的视角。医学成像中的关键点中心点人工智能(AI)研究由来已久。中心点回顾了更经典的AI方法的功能、优点和局限性，以及最近的深度学习方法。中心点从技术和临床的角度对AI对放射学的潜在影响及其评估提供了一个视角。
The recent explosion of big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. We review the functioning, strengths and limitations of more classical methods as well as of the more recent deep learning techniques. We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it.Key Points center dot Artificial intelligence (AI) research in medical imaging has a long history center dot The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods.center dot A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.