Richardson, Michael L.Lo, Hao S.2022-08-232022-08-232021-03-102021-04-12<p>Richardson ML, Adams SJ, Agarwal A, Auffermann WF, Bhattacharya AK, Consul N, Fotos JS, Kelahan LC, Lin C, Lo HS, Nguyen XV, Salkowski LR, Sin JM, Thomas RC, Wassef S, Ikuta I. Review of Artificial Intelligence Training Tools and Courses for Radiologists. Acad Radiol. 2021 Mar 10:S1076-6332(21)00061-1. doi: 10.1016/j.acra.2020.12.026. Epub ahead of print. PMID: 33714667. <a href="https://doi.org/10.1016/j.acra.2020.12.026">Link to article on publisher's site</a></p>1076-6332 (Linking)10.1016/j.acra.2020.12.02633714667https://hdl.handle.net/20.500.14038/48501<p>Full author list omitted for brevity. For the full list of authors, see article.</p>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.en-USartificial intelligencedeep learningeducationmachine learningradiologyArtificial Intelligence and RoboticsMedical EducationRadiologyJournal Articlehttps://escholarship.umassmed.edu/radiology_pubs/60222442990radiology_pubs/602