Literature DB >> 31573399

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.

J Raymond Geis1, Adrian P Brady1, Carol C Wu1, Jack Spencer1, Erik Ranschaert1, Jacob L Jaremko1, Steve G Langer1, Andrea Borondy Kitts1, Judy Birch1, William F Shields1, Robert van den Hoven van Genderen1, Elmar Kotter1, Judy Wawira Gichoya1, Tessa S Cook1, Matthew B Morgan1, An Tang1, Nabile M Safdar1, Marc Kohli1.   

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 it 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 that 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. This article is a simultaneous joint publication in Radiology, Journal of the American College of Radiology, Canadian Association of Radiologists Journal, and Insights into Imaging. Published under a CC BY-NC-ND 4.0 license. Online supplemental material is available for this article.

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Year:  2019        PMID: 31573399     DOI: 10.1148/radiol.2019191586

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  34 in total

Review 1.  Deep learning in breast radiology: current progress and future directions.

Authors:  William C Ou; Dogan Polat; Basak E Dogan
Journal:  Eur Radiol       Date:  2021-01-15       Impact factor: 5.315

2.  Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.

Authors:  John Mongan; Linda Moy; Charles E Kahn
Journal:  Radiol Artif Intell       Date:  2020-03-25

3.  A brief introduction to concepts and applications of artificial intelligence in dental imaging.

Authors:  Ruben Pauwels
Journal:  Oral Radiol       Date:  2020-08-16       Impact factor: 1.852

4.  Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.

Authors:  Francesca Coppola; Lorenzo Faggioni; Daniele Regge; Andrea Giovagnoni; Rita Golfieri; Corrado Bibbolino; Vittorio Miele; Emanuele Neri; Roberto Grassi
Journal:  Radiol Med       Date:  2020-04-29       Impact factor: 3.469

Review 5.  Artificial Intelligence: A Primer for Breast Imaging Radiologists.

Authors:  Manisha Bahl
Journal:  J Breast Imaging       Date:  2020-06-19

6.  Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.

Authors:  Joseph N Stember; Haydar Celik; David Gutman; Nathaniel Swinburne; Robert Young; Sarah Eskreis-Winkler; Andrei Holodny; Sachin Jambawalikar; Bradford J Wood; Peter D Chang; Elizabeth Krupinski; Ulas Bagci
Journal:  Radiol Artif Intell       Date:  2020-11-11

7.  The State of Radiology AI: Considerations for Purchase Decisions and Current Market Offerings.

Authors:  Yasasvi Tadavarthi; Brianna Vey; Elizabeth Krupinski; Adam Prater; Judy Gichoya; Nabile Safdar; Hari Trivedi
Journal:  Radiol Artif Intell       Date:  2020-11-11

8.  An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education.

Authors:  Merel Huisman; Erik Ranschaert; William Parker; Domenico Mastrodicasa; Martin Koci; Daniel Pinto de Santos; Francesca Coppola; Sergey Morozov; Marc Zins; Cedric Bohyn; Ural Koç; Jie Wu; Satyam Veean; Dominik Fleischmann; Tim Leiner; Martin J Willemink
Journal:  Eur Radiol       Date:  2021-05-11       Impact factor: 5.315

Review 9.  AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?

Authors:  YiRang Shin; Sungjun Kim; Young Han Lee
Journal:  Skeletal Radiol       Date:  2021-08-03       Impact factor: 2.199

Review 10.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

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