"Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board"

by Bluemke, David A; Moy, Linda; Bredella, Miriam A; Ertl-Wagner, Birgit B; Fowler, Kathryn J; Goh, Vicky J; Halpern, Elkan F; Hess, Christopher P; Schiebler, Mark L; Weiss, Clifford R (2020)

Abstract

T he number of manuscripts related to radiomics, machine learning (ML), and artificial intelligence (AI) submitted to Radiology has dramatically increased in only a few years. As expected, the number of published articles in Radiology on these topics has also increased, now representing about 25% of publications in the past year. In response to this remarkable development, the RSNA and the Radiology editorial board have responded by adding associate editors with special expertise on these topics (https://pubs.rsna.org/page/radiology/edboard). The new RSNA journal, Radiology: Artificial Intelligence, is the only journal of its type focused on radiologic imaging and AI. Given the rapid expansion of AI in imaging, it is clear that physicians and scientists in our field cannot simply relegate understanding of AI to specialists. AI will eventually touch each specialty in our discipline. The recent statement on the ethics of AI from the RSNA, American College of Radiology, and other leading imaging societies suggests that AI technology will be so universal that all radiologists using these tools need to be involved in self-education on the topic.

Keywords

Artificial Intelligence, Helath, Radiology, Algorithms, Automation

Themes

AI and Healthcare, Automation

Links to Reference

• https://doi.org/10.1148/radiol.2019192515
• http://dx.doi.org/10.1148/radiol.2019192515
• https://pubs.rsna.org/doi/full/10.1148/radiol.2019192515

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