Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery

Natural language computer applications are becoming increasingly sophisticated and, with the recent release of Generative Pre-trained Transformer 3, they could be deployed in healthcare-related contexts that have historically comprised human-to-human interaction. However, for GPT-3 and similar appli...

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Autores principales:	Diane M. Korngiebel, Sean D. Mooney
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/5d3baf6fe6234484b823a9f05b618094
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spelling	oai:doaj.org-article:5d3baf6fe6234484b823a9f05b6180942021-12-02T15:02:32ZConsidering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery10.1038/s41746-021-00464-x2398-6352https://doaj.org/article/5d3baf6fe6234484b823a9f05b6180942021-06-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00464-xhttps://doaj.org/toc/2398-6352Natural language computer applications are becoming increasingly sophisticated and, with the recent release of Generative Pre-trained Transformer 3, they could be deployed in healthcare-related contexts that have historically comprised human-to-human interaction. However, for GPT-3 and similar applications to be considered for use in health-related contexts, possibilities and pitfalls need thoughtful exploration. In this article, we briefly introduce some opportunities and cautions that would accompany advanced Natural Language Processing applications deployed in eHealth.Diane M. KorngiebelSean D. MooneyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Diane M. Korngiebel Sean D. Mooney Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery
description	Natural language computer applications are becoming increasingly sophisticated and, with the recent release of Generative Pre-trained Transformer 3, they could be deployed in healthcare-related contexts that have historically comprised human-to-human interaction. However, for GPT-3 and similar applications to be considered for use in health-related contexts, possibilities and pitfalls need thoughtful exploration. In this article, we briefly introduce some opportunities and cautions that would accompany advanced Natural Language Processing applications deployed in eHealth.
format	article
author	Diane M. Korngiebel Sean D. Mooney
author_facet	Diane M. Korngiebel Sean D. Mooney
author_sort	Diane M. Korngiebel
title	Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery
title_short	Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery
title_full	Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery
title_fullStr	Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery
title_full_unstemmed	Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery
title_sort	considering the possibilities and pitfalls of generative pre-trained transformer 3 (gpt-3) in healthcare delivery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5d3baf6fe6234484b823a9f05b618094
work_keys_str_mv	AT dianemkorngiebel consideringthepossibilitiesandpitfallsofgenerativepretrainedtransformer3gpt3inhealthcaredelivery AT seandmooney consideringthepossibilitiesandpitfallsofgenerativepretrainedtransformer3gpt3inhealthcaredelivery
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Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery

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