On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities.

Reyes, Mauricio; Meier, Raphael; Pereira, Sérgio; Silva, Carlos A; Dahlweid, Fried-Michael; von Tengg-Kobligk, Hendrik; Summers, Ronald M; Wiest, Roland (2020). On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. Radiology: artificial intelligence, 2(3), e190043. Radiological Society of North America 10.1148/ryai.2020190043

[img] Text
AI_HvTK.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (2MB) | Request a copy

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.

Item Type:

Journal Article (Review Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

UniBE Contributor:

Reyes, Mauricio; Meier, Raphael; von Tengg-Kobligk, Hendrik and Wiest, Roland

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

2638-6100

Publisher:

Radiological Society of North America

Language:

English

Submitter:

Maria de Fatima Henriques Bernardo

Date Deposited:

29 Jun 2020 18:16

Last Modified:

05 Jul 2020 02:43

Publisher DOI:

10.1148/ryai.2020190043

PubMed ID:

32510054

BORIS DOI:

10.7892/boris.144541

URI:

https://boris.unibe.ch/id/eprint/144541

Actions (login required)

Edit item Edit item
Provide Feedback