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We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology., (Copyright © 2020 The Association of University Radiologists. All rights reserved.)

Artificial intelligence (AI) systems play an increasingly important role in all parts of the imaging chain, from image creation to image interpretation to report generation. In order to responsibly manage radiology AI systems and make informed purchase decisions about them, radiologists must understand the underlying principles of AI. Our task force was formed by the Radiology Research Alliance (RRA) of the Association of University Radiologists to identify and summarize a curated list of current educational materials available for radiologists., (Copyright © 2021. Published by Elsevier Inc.)

Given the rapid pace at which modern radiology is evolving and the associated paradigm shifts in health care delivery, it is critical that radiologists adapt and constantly update the skills and knowledge required to practice safe, patient-centered care. The Association of University Radiologists-Radiology Research Alliance Lifelong Learning Task Force convened to explore the current status and future directions of lifelong learning in radiology and summarized its finding in this article. We review the various learning platforms and resources available to radiologists in their self-motivated and self-directed pursuit of lifelong learning. We also discuss the challenges and perceived barriers to lifelong learning and strategies to mitigate those barriers and optimize learning outcomes., (Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Thyroid cancer fulfills the criteria for overdiagnosis by having a reservoir of indolent cancers and practice patterns leading to the diagnosis of incidental cancers from the reservoir. The occurrence of overdiagnosis is also supported by population-based data showing an alarming rise in thyroid cancer incidence without change in mortality. Because one of the activities leading to overdiagnosis is the workup of incidental thyroid nodules detected on imaging, it is critical that radiologists understand the issue of overdiagnosis and their role in the problem and solution. This article addresses 1) essential thyroid cancer facts, 2) the evidence supporting overdiagnosis, 3) the role of radiology in overdiagnosis, 4) harms of overdiagnosis, and 5) steps radiologists can take to minimize the problem., (Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.)