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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1238-1252
Number of pages15
JournalAcademic radiology
Volume28
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • artificial intelligence
  • deep learning
  • education
  • machine learning
  • radiology

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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