Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

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Abstract

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine.

AI has great potential to increase efficiency and accuracy throughout radiology, but also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence, and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice.

This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future.

The radiology community should start now to develop codes of ethics and practice for AI which promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.

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The online version of this article (10.1186/s13244-019-0785-8) contains supplementary material, which is available to authorized users.

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Most cited references 6

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Machine Learning for Medical Imaging.

Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus … (2017)

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017.

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Implementing Machine Learning in Radiology Practice and Research.

J. Geis, Luciano M. Prevedello, Ross Filice … (2017)

The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk.

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How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis

Maria J. M. Chuquicusma, Sarfaraz Hussein, Jeremy Burt … (2018)

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Author and article information

Contributors

J. Raymond Geis:

ORCID: http://orcid.org/0000-0003-4361-6158

+1 970-980-9849 , raym.geis@gmail.com

Journal

Journal ID (nlm-ta): Insights Imaging

Journal ID (iso-abbrev): Insights Imaging

Title: Insights into Imaging

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Electronic): 1869-4101

Publication date (Electronic): 1 October 2019

Publication date PMC-release: 1 October 2019

Publication date Collection: December 2019

Volume: 10

Electronic Location Identifier: 101

Affiliations

[1 ]ISNI 0000 0004 0638 1385, GRID grid.417949.6, American College of Radiology Data Science Institute, ; Reston, Virginia USA

[2 ]ISNI 0000 0004 0396 0728, GRID grid.240341.0, Department of Radiology, , National Jewish Health, ; Denver, Colorado USA

[3 ]ISNI 0000 0004 0575 9497, GRID grid.411785.e, Mercy University Hospital, ; Cork, Ireland

[4 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, University of Texas MD Anderson Cancer Center, ; Houston, Texas USA

[5 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Department of Linguistics and Philosophy, , MIT, ; Cambridge, Massachusetts USA

[6 ]GRID grid.430814.a, Netherlands Cancer Institute, ; Amsterdam, the Netherlands

[7 ]GRID grid.17089.37, Department of Radiology and Diagnostic Imaging, , University of Alberta, ; Edmonton, Alberta Canada

[8 ]ISNI 0000 0004 0459 167X, GRID grid.66875.3a, Radiology Department-Mayo Clinic, ; Rochester, Minnesota USA

[9 ]GRID grid.419182.7, Lahey Hospital & Medical Center, ; Burlington, Massachusetts USA

[10 ]Pelvic Pain Support Network, Poole, UK

[11 ]ISNI 0000 0004 0638 1385, GRID grid.417949.6, General Counsel, American College of Radiology, ; Reston, Virginia USA

[12 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Center of Law and Internet, , Vrije Universiteit Amsterdam, ; Amsterdam, the Netherlands

[13 ]ISNI 0000 0000 9428 7911, GRID grid.7708.8, Department of Radiology, , University Medical Center, ; Freiburg, Germany

[14 ]ISNI 0000 0000 9758 5690, GRID grid.5288.7, Department of Interventional Radiology, , Oregon Health & Science University, ; Portland, Oregon USA

[15 ]ISNI 0000 0001 0941 6502, GRID grid.189967.8, Department of Radiology and Imaging Sciences, , Emory University, ; Atlanta, Georgia USA

[16 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Radiology, , University of Pennsylvania, ; Philadelphia, Pennsylvania USA

[17 ]ISNI 0000 0001 2193 0096, GRID grid.223827.e, Department of Radiology and Imaging Sciences, , University of Utah, ; Salt Lake City, Utah USA

[18 ]ISNI 0000 0001 0743 2111, GRID grid.410559.c, Centre de Recherche du Centre Hospitalier de L’Université de Montréal, ; Quebec, Canada

[19 ]ISNI 0000 0001 2297 6811, GRID grid.266102.1, Department of Radiology and Biomedical Imaging, , UCSF, ; San Francisco, California USA

Author information

J. Raymond Geis http://orcid.org/0000-0003-4361-6158

Article

Publisher ID: 785

DOI: 10.1186/s13244-019-0785-8

PMC ID: 6768929

PubMed ID: 31571015

SO-VID: 5a8a11d1-abf5-4dc9-8742-20a7f966634a

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

History

Date received : 8 August 2019

Date accepted : 26 August 2019

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ScienceOpen disciplines: Radiology & Imaging

Keywords: ethics,artificial intelligence,radiology,machine learning,data

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ScienceOpen disciplines: Radiology & Imaging

Keywords: ethics, artificial intelligence, radiology, machine learning, data

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Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement

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Most cited references 6

Machine Learning for Medical Imaging.

Implementing Machine Learning in Radiology Practice and Research.

How to fool radiologists with generative adversarial networks? A visual turing test for lung cancer diagnosis

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