Artificial intelligence in radiology

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forw...

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Bibliographic Details
Published in:Nature reviews. Cancer Vol. 18; no. 8; pp. 500 - 510
Main Authors: Hosny, Ahmed, Parmar, Chintan, Quackenbush, John, Schwartz, Lawrence H., Aerts, Hugo J. W. L.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.08.2018
Nature Publishing Group
Subjects:
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59/57
59/78
631/114/1305
631/67/2321
631/67/2322
692/700/1421
Algorithms
Artificial Intelligence
Biomedical and Life Sciences
Biomedicine
Cancer Research
Deep Learning
Humans
Image processing
Image Processing, Computer-Assisted
Machine Learning
Medical imaging equipment
Medical practice
Medical schools
Natural language processing
Neoplasms - diagnostic imaging
Neural networks
Pattern Recognition, Automated
Perspective
Physicians
Radiology
Technology application
ISSN:1474-175X, 1474-1768, 1474-1768
Online Access:Get full text
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Summary:Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced. In this Opinion article, Hosny et al. discuss the application of artificial intelligence to image-based tasks in the field of radiology and consider the advantages and challenges of its clinical implementation.
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Hugo_Aerts@dfci.harvard.edu
A.H., C.P. and H.J.W.L.A. performed the literature survey, curated the content and general direction and w rote the manuscript. J.Q. and L.H.S. provided substantial contributions to discussions of the content. All authors contributed to reviewing and editing the manuscript before submission.
Author contributions
ISSN:1474-175X
1474-1768
1474-1768
DOI:10.1038/s41568-018-0016-5