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Potential reduction in healthcare carbon footprint by autonomous artificial intelligence

Healthcare is a large contributor to greenhouse gas (GHG) emissions around the world, given current power generation mix. Telemedicine, with its reduced travel for providers and patients, has been proposed to reduce emissions. Artificial intelligence (AI), and especially autonomous AI, where the med...

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Autores principales: Wolf, Risa M., Abramoff, Michael D., Channa, Roomasa, Tava, Chris, Clarida, Warren, Lehmann, Harold P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098499/
https://www.ncbi.nlm.nih.gov/pubmed/35551275
http://dx.doi.org/10.1038/s41746-022-00605-w
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author Wolf, Risa M.
Abramoff, Michael D.
Channa, Roomasa
Tava, Chris
Clarida, Warren
Lehmann, Harold P.
author_facet Wolf, Risa M.
Abramoff, Michael D.
Channa, Roomasa
Tava, Chris
Clarida, Warren
Lehmann, Harold P.
author_sort Wolf, Risa M.
collection PubMed
description Healthcare is a large contributor to greenhouse gas (GHG) emissions around the world, given current power generation mix. Telemedicine, with its reduced travel for providers and patients, has been proposed to reduce emissions. Artificial intelligence (AI), and especially autonomous AI, where the medical decision is made without human oversight, has the potential to further reduce healthcare GHG emissions, but concerns have also been expressed about GHG emissions from digital technology, and AI training and inference. In a real-world example, we compared the marginal GHG contribution of an encounter performed by an autonomous AI to that of an in-person specialist encounter. Results show that an 80% reduction may be achievable, and we conclude that autonomous AI has the potential to reduce healthcare GHG emissions.
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spelling pubmed-90984992022-05-14 Potential reduction in healthcare carbon footprint by autonomous artificial intelligence Wolf, Risa M. Abramoff, Michael D. Channa, Roomasa Tava, Chris Clarida, Warren Lehmann, Harold P. NPJ Digit Med Comment Healthcare is a large contributor to greenhouse gas (GHG) emissions around the world, given current power generation mix. Telemedicine, with its reduced travel for providers and patients, has been proposed to reduce emissions. Artificial intelligence (AI), and especially autonomous AI, where the medical decision is made without human oversight, has the potential to further reduce healthcare GHG emissions, but concerns have also been expressed about GHG emissions from digital technology, and AI training and inference. In a real-world example, we compared the marginal GHG contribution of an encounter performed by an autonomous AI to that of an in-person specialist encounter. Results show that an 80% reduction may be achievable, and we conclude that autonomous AI has the potential to reduce healthcare GHG emissions. Nature Publishing Group UK 2022-05-12 /pmc/articles/PMC9098499/ /pubmed/35551275 http://dx.doi.org/10.1038/s41746-022-00605-w Text en © The Author(s) 2022 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 Comment
Wolf, Risa M.
Abramoff, Michael D.
Channa, Roomasa
Tava, Chris
Clarida, Warren
Lehmann, Harold P.
Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
title Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
title_full Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
title_fullStr Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
title_full_unstemmed Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
title_short Potential reduction in healthcare carbon footprint by autonomous artificial intelligence
title_sort potential reduction in healthcare carbon footprint by autonomous artificial intelligence
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098499/
https://www.ncbi.nlm.nih.gov/pubmed/35551275
http://dx.doi.org/10.1038/s41746-022-00605-w
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