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Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields oft...

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Autores principales: Oniani, David, Hilsman, Jordan, Peng, Yifan, Poropatich, Ronald K., Pamplin, Jeremy C., Legault, Gary L., Wang, Yanshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693640/
https://www.ncbi.nlm.nih.gov/pubmed/38042910
http://dx.doi.org/10.1038/s41746-023-00965-x
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author Oniani, David
Hilsman, Jordan
Peng, Yifan
Poropatich, Ronald K.
Pamplin, Jeremy C.
Legault, Gary L.
Wang, Yanshan
author_facet Oniani, David
Hilsman, Jordan
Peng, Yifan
Poropatich, Ronald K.
Pamplin, Jeremy C.
Legault, Gary L.
Wang, Yanshan
author_sort Oniani, David
collection PubMed
description In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice.
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spelling pubmed-106936402023-12-04 Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare Oniani, David Hilsman, Jordan Peng, Yifan Poropatich, Ronald K. Pamplin, Jeremy C. Legault, Gary L. Wang, Yanshan NPJ Digit Med Perspective In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has garnered a lot of attention in the medical research community, leading to debates about its application in the healthcare sector, mainly due to concerns about transparency and related issues. Meanwhile, questions around the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied. As a result, there are no clear solutions to address ethical concerns, and decision-makers often neglect to consider the significance of ethical principles before implementing generative AI in clinical practice. In an attempt to address these issues, we explore ethical principles from the military perspective and propose the “GREAT PLEA” ethical principles, namely Governability, Reliability, Equity, Accountability, Traceability, Privacy, Lawfulness, Empathy, and Eutonomy, for generative AI in healthcare. Furthermore, we introduce a framework for adopting and expanding these ethical principles in a practical way that has been useful in the military and can be applied to healthcare for generative AI, based on contrasting their ethical concerns and risks. Ultimately, we aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI into healthcare practice. Nature Publishing Group UK 2023-12-02 /pmc/articles/PMC10693640/ /pubmed/38042910 http://dx.doi.org/10.1038/s41746-023-00965-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Perspective
Oniani, David
Hilsman, Jordan
Peng, Yifan
Poropatich, Ronald K.
Pamplin, Jeremy C.
Legault, Gary L.
Wang, Yanshan
Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
title Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
title_full Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
title_fullStr Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
title_full_unstemmed Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
title_short Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
title_sort adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10693640/
https://www.ncbi.nlm.nih.gov/pubmed/38042910
http://dx.doi.org/10.1038/s41746-023-00965-x
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