Review ArticleArtificial Intelligence and Machine Learning in Radiology Current State and Considerations for Routine Clinical ImplementationWichmann, Julian L. MD∗,†; Willemink, Martin J. MD, PhD‡; De Cecco, Carlo N. MD, PhD§Author Information From the ∗Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main †Siemens Healthineers, Erlangen, Germany ‡Department of Radiology, Stanford University School of Medicine, Stanford, CA §Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA. Received for publication February 5, 2020; and accepted for publication, after revision, February 18, 2020. Conflicts of interest and sources of funding: Julian L. Wichmann is an employee of Siemens Healthineers, has received speaker's fees from Siemens Healthcare and GE Healthcare, and is an investor in Segmed, Inc. Martin J. Willemink is a cofounder/shareholder of Segmed, Inc. Activities not related to the present article: research grants from American Heart Association (18POST34030192), Philips Healthcare, and Stanford University; consulting for Arterys, Inc. Carlo N. De Cecco receives institutional research support and/or honorarium as speaker from Siemens Healthineers. Correspondence to: Julian L. Wichmann, MD, Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany. E-mail: [email protected]. Investigative Radiology: September 2020 - Volume 55 - Issue 9 - p 619-627 doi: 10.1097/RLI.0000000000000673 Buy Metrics Abstract Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques to develop new algorithms. Interestingly, this innovation is driven simultaneously by academia, existing global medical device vendors, and—fueled by venture capital—recently founded startups. Radiologists find themselves once again in the position to lead this innovation to improve clinical workflows and ultimately patient outcome. However, although the end of today's radiologists' profession has been proclaimed multiple times, routine clinical application of such AI algorithms in 2020 remains rare. The goal of this review article is to describe in detail the relevance of appropriate imaging data as a bottleneck for innovation, provide insights into the many obstacles for technical implementation, and give additional perspectives to radiologists who often view AI solely from their clinical role. As regulatory approval processes for such medical devices are currently under public discussion and the relevance of imaging data is transforming, radiologists need to establish themselves as the leading gatekeepers for evolution of their field and be aware of the many stakeholders and sometimes conflicting interests. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.