Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine
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Abstract
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.
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Schmälzle & Wilcox highlight the risks of a dual use of LLMs: while they might further equal access to information, malicious actors can and seem to be using LLMs to spread fake information and devise health messages at an unprecedented scale that is harmful to societies.
“Schmälzle & Wilcox highlight the risks of a dual use of LLMs50. While they might further equal access to information, malicious actors can and seem to be using LLMs to spread fake information and devise health messages at an unprecedented scale that is harmful to societies50,51,75.”
From The ethics of ChatGPT in medicine and healthcare: a systematic review on large language models (LLMs) (2024) · cites this paper as [50]
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- Authors
- Schmälzle, R., & Wilcox, S.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Schmälzle, Ralf
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-01-18
- Volume
- 24
- Issue
- 1
- Pages
- e28858
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8808340
- OpenAlex
- W3217185924
- Cited by
- 43
- References
- 95
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 12 · 2023: 6
- FWCI
- 2.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Communication › Public Relations and Crisis Communication
- Keywords
- folic acid, health communication, social media campaigns, public awareness
- MeSH terms
- Artificial Intelligence, Folic Acid, Humans, Persuasive Communication, Health Communication, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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