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Generative AI as a Tool for Environmental Health Research Translation

One valuable application for generative artificial intelligence (AI) is summarizing research studies for non‐academic readers. We submitted five articles to Chat Generative Pre‐trained Transformer (ChatGPT) for summarization, and asked the article's author to rate the summaries. Higher ratings...

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Detalles Bibliográficos
Autores principales: Anderson, Lauren B., Kanneganti, Dhiraj, Houk, Mary Bentley, Holm, Rochelle H., Smith, Ted
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369501/
https://www.ncbi.nlm.nih.gov/pubmed/37502196
http://dx.doi.org/10.1029/2023GH000875
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author Anderson, Lauren B.
Kanneganti, Dhiraj
Houk, Mary Bentley
Holm, Rochelle H.
Smith, Ted
author_facet Anderson, Lauren B.
Kanneganti, Dhiraj
Houk, Mary Bentley
Holm, Rochelle H.
Smith, Ted
author_sort Anderson, Lauren B.
collection PubMed
description One valuable application for generative artificial intelligence (AI) is summarizing research studies for non‐academic readers. We submitted five articles to Chat Generative Pre‐trained Transformer (ChatGPT) for summarization, and asked the article's author to rate the summaries. Higher ratings were assigned to more insight‐oriented activities, such as the production of eighth‐grade reading level summaries, and summaries highlighting the most important findings and real‐world applications. The general summary request was rated lower. For the field of environmental health science, no‐cost AI technology such as ChatGPT holds the promise to improve research translation, but it must continue to be improved (or improve itself) from its current capability.
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spelling pubmed-103695012023-07-27 Generative AI as a Tool for Environmental Health Research Translation Anderson, Lauren B. Kanneganti, Dhiraj Houk, Mary Bentley Holm, Rochelle H. Smith, Ted Geohealth Commentary One valuable application for generative artificial intelligence (AI) is summarizing research studies for non‐academic readers. We submitted five articles to Chat Generative Pre‐trained Transformer (ChatGPT) for summarization, and asked the article's author to rate the summaries. Higher ratings were assigned to more insight‐oriented activities, such as the production of eighth‐grade reading level summaries, and summaries highlighting the most important findings and real‐world applications. The general summary request was rated lower. For the field of environmental health science, no‐cost AI technology such as ChatGPT holds the promise to improve research translation, but it must continue to be improved (or improve itself) from its current capability. John Wiley and Sons Inc. 2023-07-26 /pmc/articles/PMC10369501/ /pubmed/37502196 http://dx.doi.org/10.1029/2023GH000875 Text en © 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. 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 Commentary
Anderson, Lauren B.
Kanneganti, Dhiraj
Houk, Mary Bentley
Holm, Rochelle H.
Smith, Ted
Generative AI as a Tool for Environmental Health Research Translation
title Generative AI as a Tool for Environmental Health Research Translation
title_full Generative AI as a Tool for Environmental Health Research Translation
title_fullStr Generative AI as a Tool for Environmental Health Research Translation
title_full_unstemmed Generative AI as a Tool for Environmental Health Research Translation
title_short Generative AI as a Tool for Environmental Health Research Translation
title_sort generative ai as a tool for environmental health research translation
topic Commentary
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369501/
https://www.ncbi.nlm.nih.gov/pubmed/37502196
http://dx.doi.org/10.1029/2023GH000875
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