Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

Authors

Ahmed W. Moawad
David T. Fuentes, Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.
Mohamed G. ElBanan, Department of Diagnostic and Interventional Radiology, Yale New Haven Health, Bridgeport Hospital, CT.Follow
Ahmed S. Shalaby, From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.
Jeffrey Guccione, Department of Diagnostic and Interventional Imaging, The University of Texas Health Sciences Center at Houston, Houston, TX.
Serageldin Kamel, Clinical Neurosciences Imaging Center, Yale University School of Medicine, New Haven, CT.
Corey T. Jensen, From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.Follow
Khaled M. Elsayes, From the Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.Follow

Document Type

Article

Publication Title

Journal of computer assisted tomography

Abstract

Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

First Page

78

Last Page

90

DOI

10.1097/RCT.0000000000001247

Publication Date

1-1-2022

Recommended Citation

Moawad, Ahmed W.; Fuentes, David T.; ElBanan, Mohamed G.; Shalaby, Ahmed S.; Guccione, Jeffrey; Kamel, Serageldin; Jensen, Corey T.; and Elsayes, Khaled M., "Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities" (2022). Radiology. 42.
https://scholar.bridgeporthospital.org/radiology/42

Identifier

35027520 (pubmed); 10.1097/RCT.0000000000001247 (doi); 00004728-900000000-98833 (pii)