Literature DB >> 30962048

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Jacob L Jaremko1, Marleine Azar2, Rebecca Bromwich3, Andrea Lum4, Li Hsia Alicia Cheong5, Martin Gibert6, François Laviolette7, Bruce Gray8, Caroline Reinhold9, Mark Cicero10, Jaron Chong9, James Shaw11, Frank J Rybicki12, Casey Hurrell13, Emil Lee14, An Tang15.   

Abstract

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Ethics; Imaging; Legal; Machine learning; Radiology

Mesh:

Year:  2019        PMID: 30962048     DOI: 10.1016/j.carj.2019.03.001

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


  21 in total

1.  Identifying Ethical Considerations for Machine Learning Healthcare Applications.

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5.  The future of artificial intelligence in medicine: Medical-legal considerations for health leaders.

Authors:  Sunam Jassar; Scott J Adams; Amy Zarzeczny; Brent E Burbridge
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6.  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

7.  Artificial Intelligence in Radiology-Ethical Considerations.

Authors:  Adrian P Brady; Emanuele Neri
Journal:  Diagnostics (Basel)       Date:  2020-04-17

Review 8.  Clinical Information Systems - Seen through the Ethics Lens.

Authors:  Ursula H Hübner; Nicole Egbert; Georg Schulte
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Review 9.  Imaging in the COVID-19 era: Lessons learned during a pandemic.

Authors:  Georgios Antonios Sideris; Melina Nikolakea; Aikaterini-Eleftheria Karanikola; Sofia Konstantinopoulou; Dimitrios Giannis; Lucy Modahl
Journal:  World J Radiol       Date:  2021-06-28

10.  Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

Authors:  Yoganand Balagurunathan; Andrew Beers; Michael Mcnitt-Gray; Lubomir Hadjiiski; Sandy Napel; Dmitry Goldgof; Gustavo Perez; Pablo Arbelaez; Alireza Mehrtash; Tina Kapur; Ehwa Yang; Jung Won Moon; Gabriel Bernardino Perez; Ricard Delgado-Gonzalo; M Mehdi Farhangi; Amir A Amini; Renkun Ni; Xue Feng; Aditya Bagari; Kiran Vaidhya; Benjamin Veasey; Wiem Safta; Hichem Frigui; Joseph Enguehard; Ali Gholipour; Laura Silvana Castillo; Laura Alexandra Daza; Paul Pinsky; Jayashree Kalpathy-Cramer; Keyvan Farahani
Journal:  IEEE Trans Med Imaging       Date:  2021-11-30       Impact factor: 11.037

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