Literature DB >> 31585825

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

Mesh:

Year:  2019        PMID: 31585825     DOI: 10.1016/j.carj.2019.08.010

Source DB:  PubMed          Journal:  Can Assoc Radiol J        ISSN: 0846-5371            Impact factor:   2.248


  15 in total

Review 1.  Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice.

Authors:  Guy Fagherazzi; Aurélie Fischer; Muhannad Ismael; Vladimir Despotovic
Journal:  Digit Biomark       Date:  2021-04-16

Review 2.  On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities.

Authors:  Mauricio Reyes; Raphael Meier; Sérgio Pereira; Carlos A Silva; Fried-Michael Dahlweid; Hendrik von Tengg-Kobligk; Ronald M Summers; Roland Wiest
Journal:  Radiol Artif Intell       Date:  2020-05-27

Review 3.  Trustworthy Artificial Intelligence in Medical Imaging.

Authors:  Navid Hasani; Michael A Morris; Arman Rhamim; Ronald M Summers; Elizabeth Jones; Eliot Siegel; Babak Saboury
Journal:  PET Clin       Date:  2022-01

Review 4.  Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.

Authors:  Ling Yang; Ioana Cezara Ene; Reza Arabi Belaghi; David Koff; Nina Stein; Pasqualina Lina Santaguida
Journal:  Eur Radiol       Date:  2021-09-21       Impact factor: 5.315

5.  Artificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation.

Authors:  Alan C Kwan; Gerran Salto; Susan Cheng; David Ouyang
Journal:  Curr Cardiovasc Risk Rep       Date:  2021-08-04

6.  On Interpretability of Artificial Neural Networks: A Survey.

Authors:  Feng-Lei Fan; Jinjun Xiong; Mengzhou Li; Ge Wang
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2021-03-17

7.  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

8.  Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges.

Authors:  Thomas Weikert; Marco Francone; Suhny Abbara; Bettina Baessler; Byoung Wook Choi; Matthias Gutberlet; Elizabeth M Hecht; Christian Loewe; Elie Mousseaux; Luigi Natale; Konstantin Nikolaou; Karen G Ordovas; Charles Peebles; Claudia Prieto; Rodrigo Salgado; Birgitta Velthuis; Rozemarijn Vliegenthart; Jens Bremerich; Tim Leiner
Journal:  Eur Radiol       Date:  2020-11-19       Impact factor: 5.315

9.  Ethical Implications of Alzheimer's Disease Prediction in Asymptomatic Individuals through Artificial Intelligence.

Authors:  Frank Ursin; Cristian Timmermann; Florian Steger
Journal:  Diagnostics (Basel)       Date:  2021-03-04

Review 10.  Role of Chest Imaging in Viral Lung Diseases.

Authors:  Diletta Cozzi; Eleonora Bicci; Alessandra Bindi; Edoardo Cavigli; Ginevra Danti; Michele Galluzzo; Vincenza Granata; Silvia Pradella; Margherita Trinci; Vittorio Miele
Journal:  Int J Environ Res Public Health       Date:  2021-06-14       Impact factor: 3.390

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