Cargando…

Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine

BACKGROUND: Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. OBJECTIVE: In this study, we aim to harness the...

Descripción completa

Detalles Bibliográficos
Autores principales: Schmälzle, Ralf, Wilcox, Shelby
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808340/
https://www.ncbi.nlm.nih.gov/pubmed/35040800
http://dx.doi.org/10.2196/28858
_version_ 1784643866773684224
author Schmälzle, Ralf
Wilcox, Shelby
author_facet Schmälzle, Ralf
Wilcox, Shelby
author_sort Schmälzle, Ralf
collection PubMed
description BACKGROUND: Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. OBJECTIVE: In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness. METHODS: We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages. RESULTS: We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness. CONCLUSIONS: The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage.
format Online
Article
Text
id pubmed-8808340
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-88083402022-02-04 Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine Schmälzle, Ralf Wilcox, Shelby J Med Internet Res Original Paper BACKGROUND: Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. OBJECTIVE: In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness. METHODS: We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages. RESULTS: We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness. CONCLUSIONS: The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage. JMIR Publications 2022-01-18 /pmc/articles/PMC8808340/ /pubmed/35040800 http://dx.doi.org/10.2196/28858 Text en ©Ralf Schmälzle, Shelby Wilcox. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.01.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Schmälzle, Ralf
Wilcox, Shelby
Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
title Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
title_full Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
title_fullStr Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
title_full_unstemmed Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
title_short Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
title_sort harnessing artificial intelligence for health message generation: the folic acid message engine
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808340/
https://www.ncbi.nlm.nih.gov/pubmed/35040800
http://dx.doi.org/10.2196/28858
work_keys_str_mv AT schmalzleralf harnessingartificialintelligenceforhealthmessagegenerationthefolicacidmessageengine
AT wilcoxshelby harnessingartificialintelligenceforhealthmessagegenerationthefolicacidmessageengine