Review of Artificial Intelligence Training Tools and Courses for Radiologists

Michael L Richardson,Scott J Adams,Atul Agarwal,William F Auffermann,Anup K Bhattacharya,Nikita Consul,Joseph S Fotos,Linda C Kelahan,Christine Lin,Hao S Lo,Xuan V Nguyen,Lonie R Salkowski,Jessica M Sin,Robert C Thomas,Shafik Wassef,Ichiro Ikuta

doi:10.1016/j.acra.2020.12.026

Review of Artificial Intelligence Training Tools and Courses for Radiologists

Michael L Richardson, Scott J Adams + Show 14 more

https://doi.org/10.1016/j.acra.2020.12.026

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Journal: Academic Radiology	Publication Date: Mar 11, 2021
Citations: 9

Affiliation: University of Washington, University of Saskatchewan, Indiana University Indianapolis, University of Utah, Baylor College of Medicine, Hershey (United States), Northwestern University, University Radiology, The Ohio State University Wexner Medical Center, University of Wisconsin–Madison, Louisiana State University, Yale University

#Radiology Research Alliance #Artificial Intelligence Systems + Show 8 more

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Abstract

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.

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