Literature DB >> 31585696

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

J Raymond Geis1, Adrian P Brady2, Carol C Wu3, Jack Spencer4, Erik Ranschaert5, Jacob L Jaremko6, Steve G Langer7, Andrea Borondy Kitts8, Judy Birch9, William F Shields10, Robert van den Hoven van Genderen11, Elmar Kotter12, Judy Wawira Gichoya13, Tessa S Cook14, Matthew B Morgan15, An Tang16, Nabile M Safdar17, Marc Kohli18.   

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.
Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; data; ethics; machine learning; radiology

Year:  2019        PMID: 31585696     DOI: 10.1016/j.jacr.2019.07.028

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  14 in total

1.  Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future.

Authors:  Eyal Lotan; Charlotte Tschider; Daniel K Sodickson; Arthur L Caplan; Mary Bruno; Ben Zhang; Yvonne W Lui
Journal:  J Am Coll Radiol       Date:  2020-04-28       Impact factor: 5.532

Review 2.  Neuroimaging in the Era of Artificial Intelligence: Current Applications.

Authors:  Robert Monsour; Mudit Dutta; Ahmed-Zayn Mohamed; Andrew Borkowski; Narayan A Viswanadhan
Journal:  Fed Pract       Date:  2022-04-12

Review 3.  A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

Authors:  Simone Vicini; Chandra Bortolotto; Marco Rengo; Daniela Ballerini; Davide Bellini; Iacopo Carbone; Lorenzo Preda; Andrea Laghi; Francesca Coppola; Lorenzo Faggioni
Journal:  Radiol Med       Date:  2022-06-30       Impact factor: 6.313

Review 4.  Artificial Intelligence in Interventional Radiology.

Authors:  Joseph R Kallini; John M Moriarty
Journal:  Semin Intervent Radiol       Date:  2022-08-31       Impact factor: 1.780

5.  Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies.

Authors:  Lushun Jiang; Zhe Wu; Xiaolan Xu; Yaqiong Zhan; Xuehang Jin; Li Wang; Yunqing Qiu
Journal:  J Int Med Res       Date:  2021-03       Impact factor: 1.671

Review 6.  Challenges and opportunities for artificial intelligence in oncological imaging.

Authors:  H M C Cheung; D Rubin
Journal:  Clin Radiol       Date:  2021-04-24       Impact factor: 3.389

7.  A No-Math Primer on the Principles of Machine Learning for Radiologists.

Authors:  Matthew D Lee; Mohammed Elsayed; Sumit Chopra; Yvonne W Lui
Journal:  Semin Ultrasound CT MR       Date:  2022-02-11       Impact factor: 1.641

Review 8.  From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care.

Authors:  Ashley C Griffin; Umit Topaloglu; Sean Davis; Arlene E Chung
Journal:  Yearb Med Inform       Date:  2020-08-21

Review 9.  Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis.

Authors:  Giuseppe Muscogiuri; Marly Van Assen; Christian Tesche; Carlo N De Cecco; Mattia Chiesa; Stefano Scafuri; Marco Guglielmo; Andrea Baggiano; Laura Fusini; Andrea I Guaricci; Mark G Rabbat; Gianluca Pontone
Journal:  Biomed Res Int       Date:  2020-12-16       Impact factor: 3.411

10.  Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives.

Authors:  Simon Lennartz; Thomas Dratsch; David Zopfs; Thorsten Persigehl; David Maintz; Nils Große Hokamp; Daniel Pinto Dos Santos
Journal:  J Med Internet Res       Date:  2021-02-17       Impact factor: 5.428

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