Abdominopelvic Imaging: GastrointestinalCurrent Status of Radiomics and Deep Learning in Liver ImagingChu, Linda C. MD∗; Park, Seyoun PhD∗; Kawamoto, Satomi MD∗; Yuille, Alan L. PhD†; Hruban, Ralph H. MD‡; Fishman, Elliot K. MD∗Author Information From the ∗The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Johns Hopkins University School of Medicine †Department of Computer Science, Johns Hopkins University ‡Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD. Received for publication January 13, 2021; accepted March 11, 2021. Correspondence to: Linda C. Chu, MD, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Johns Hopkins University School of Medicine, 600 N Wolfe St, Hal B168, Baltimore, MD 21287 (e-mail: [email protected]). L.C.C., S.P., S.K., A.L.Y., and E.K.F. received research support from the Lustgarten Foundation. L.C.C., S.P., and E.K.F. received additional research support from the Emerson Collective. L.C.C. receives grant support from the Lustgarten Foundation and the Emerson Collective. S.P. receives grant support from the Lustgarten Foundation. S.K. receives grant support from the Lustgarten Foundation. A.L.Y. receives grant support from the Lustgarten Foundation. R.H.H. receives grant support from the National Institutes of Health. E.K.F. reports grant support from the Lustgarten Foundation, Siemens, GE, and is the cofounder of HipGraphics. Journal of Computer Assisted Tomography: 5/6 2021 - Volume 45 - Issue 3 - p 343-351 doi: 10.1097/RCT.0000000000001169 Buy Metrics Abstract Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.