Journal of the American College of Radiology : JACR
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Artificial intelligence (AI) is an exciting technology that can transform the practice of radiology. However, radiology AI is still immature with limited adopters, dominated by academic institutions, and few use cases in general practice. With scale and a focus on innovation, our practice has had the opportunity to be an early adopter of AI technology. ⋯ With this experience, our practice has both managed challenges and identified unexpected benefits of AI. To ensure a successful and scalable AI implementation, multiple steps are required, including preparing the data, systems, and radiologists. This article reviews our experience with AI and describes why each step is important.
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The radiology workflow can be segmented into three large groups: pre-interpretative processes, interpretation, and postinterpretative processes. Each stage of this workflow represents quality improvement opportunities for artificial intelligence and machine learning. Although the focus of recent research has been targeted toward optimization of image interpretation, this article describes significant use cases for artificial intelligence in both the pre-interpretative and postinterpretative aspects of radiology. We provide examples of how current applications of AI for quality improvement purposes across the radiology workflow have been implemented and how further integration of these technologies can significantly improve clinical efficiency, reduce radiologist work burden, and ultimately optimize patient care and outcomes.
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In the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain from test ordering to report communication. AI has been proposed as a means to optimize patient scheduling, improve worklist management, enhance image acquisition, and help radiologists interpret diagnostic studies. ⋯ Moreover, although the current use of AI in radiology has emphasized its clinical applications, some of which are in the distant future, it is increasingly clear that AI algorithms could also be used in the more immediate future for a variety of noninterpretive and quality improvement uses. Such uses include the integration of AI into electronic health record systems to reduce unwarranted variation in radiologists' follow-up recommendations and to improve other dimensions of radiology report quality. In the end, the potential of AI in radiology must be balanced with acknowledgment of its current limitations regarding generalizability and data privacy.
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Comparative Study Observational Study
Collateral Damage: The Impact of the COVID-19 Pandemic on Acute Abdominal Emergency Presentations.
In March 2020, the World Health Organization declared a pandemic caused by a novel coronavirus. Public information created awareness as well as concern in the general population. There has been a reported decrease in the number of patients attending emergency departments (ED) during the pandemic. This is the first study to determine differences in the types of presenting illnesses, severity, and rate of resultant surgical intervention during the pandemic. ⋯ To date, there is little published data regarding the presentation and severity of illnesses during the coronavirus disease 2019 pandemic. This information has important public health implications, highlighting the need to educate patients to continue to present to hospital services during such crises, including if a purported second wave of COVID-19 arises.
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Retraction Of Publication
TEMPORARY REMOVAL: Radiology Extenders: Impact on Throughput and Accuracy for Routine Chest Radiographs.
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.