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On the interpretability of artificial intelligence in radiology: challenges and opportunities
Authors
Fried-Michael Dahlweid
Raphael Meier
+6 more
Sérgio Pereira
Mauricio Reyes
Carlos A. Silva
Ronald M Summers
Hendrik von Tengg-Kobligk
Roland Wiest
Publication date
27 May 2020
Publisher
'Radiological Society of North America (RSNA)'
Doi
Cite
Abstract
As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts. Toward this goal, approaches to make AI "interpretable" have gained attention to enhance the understanding of a machine learning algorithm, despite its complexity. This article aims to provide insights into the current state of the art of interpretability methods for radiology AI. This review discusses radiologists' opinions on the topic and suggests trends and challenges that need to be addressed to effectively streamline interpretability methods in clinical practice. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Gastounioti and Kontos in this issue.NIH -National Institutes of Health(1Z01 CL040004
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Universidade do Minho: RepositoriUM
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oai:repositorium.sdum.uminho.p...
Last time updated on 12/04/2021
Bern Open Repository and Information System (BORIS)
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oai:boris.unibe.ch:144541
Last time updated on 19/11/2020