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First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology
Artificial Intelligence and machine learning are novel technologies that will change the way veterinary medicine is practiced. Exactly how this change will occur is yet to be determined, and, as is the nature with disruptive technologies, will be difficult to predict. Ushering in this new tool in a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10107688/ https://www.ncbi.nlm.nih.gov/pubmed/36514231 http://dx.doi.org/10.1111/vru.13171 |
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author | Cohen, Eli B. Gordon, Ira K. |
author_facet | Cohen, Eli B. Gordon, Ira K. |
author_sort | Cohen, Eli B. |
collection | PubMed |
description | Artificial Intelligence and machine learning are novel technologies that will change the way veterinary medicine is practiced. Exactly how this change will occur is yet to be determined, and, as is the nature with disruptive technologies, will be difficult to predict. Ushering in this new tool in a conscientious way will require knowledge of the terminology and types of AI as well as forward thinking regarding the ethical and legal implications within the profession. Developers as well as end users will need to consider the ethical and legal components alongside functional creation of algorithms in order to foster acceptance and adoption, and most importantly to prevent patient harm. There are key differences in deployment of these technologies in veterinary medicine relative to human healthcare, namely our ability to perform euthanasia, and the lack of regulatory validation to bring these technologies to market. These differences along with others create a much different landscape than AI use in human medicine, and necessitate proactive planning in order to prevent catastrophic outcomes, encourage development and adoption, and protect the profession from unnecessary liability. The authors offer that deploying these technologies prior to considering the larger ethical and legal implications and without stringent validation is putting the AI cart before the horse, and risks putting patients and the profession in harm's way. |
format | Online Article Text |
id | pubmed-10107688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101076882023-04-18 First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology Cohen, Eli B. Gordon, Ira K. Vet Radiol Ultrasound Foundational Principles of Ai Artificial Intelligence and machine learning are novel technologies that will change the way veterinary medicine is practiced. Exactly how this change will occur is yet to be determined, and, as is the nature with disruptive technologies, will be difficult to predict. Ushering in this new tool in a conscientious way will require knowledge of the terminology and types of AI as well as forward thinking regarding the ethical and legal implications within the profession. Developers as well as end users will need to consider the ethical and legal components alongside functional creation of algorithms in order to foster acceptance and adoption, and most importantly to prevent patient harm. There are key differences in deployment of these technologies in veterinary medicine relative to human healthcare, namely our ability to perform euthanasia, and the lack of regulatory validation to bring these technologies to market. These differences along with others create a much different landscape than AI use in human medicine, and necessitate proactive planning in order to prevent catastrophic outcomes, encourage development and adoption, and protect the profession from unnecessary liability. The authors offer that deploying these technologies prior to considering the larger ethical and legal implications and without stringent validation is putting the AI cart before the horse, and risks putting patients and the profession in harm's way. John Wiley and Sons Inc. 2022-12-13 2022-12 /pmc/articles/PMC10107688/ /pubmed/36514231 http://dx.doi.org/10.1111/vru.13171 Text en © 2022 The Authors. Veterinary Radiology & Ultrasound published by Wiley Periodicals LLC on behalf of American College of Veterinary Radiology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Foundational Principles of Ai Cohen, Eli B. Gordon, Ira K. First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
title | First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
title_full | First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
title_fullStr | First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
title_full_unstemmed | First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
title_short | First, do no harm. Ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
title_sort | first, do no harm. ethical and legal issues of artificial intelligence and machine learning in veterinary radiology and radiation oncology |
topic | Foundational Principles of Ai |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10107688/ https://www.ncbi.nlm.nih.gov/pubmed/36514231 http://dx.doi.org/10.1111/vru.13171 |
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