mlsanityData to 7 Oct 2026

Artificial Intelligence for Health Message Generation: Theory, Method, and an Empirical Study Using Prompt Engineering

arXiv 2212.07507 ↗First posted 14 Dec 2022cs.CLcited by 14

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Abstract (arXiv)

This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. Using prompt engineering, we generated messages that could be used to raise awareness and compared them to retweeted human-generated messages via computational and human evaluation methods. The system was easy to use and prolific, and computational analyses revealed that the AI-generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Also, the human evaluation study showed that AI-generated messages ranked higher in message quality and clarity. We discuss the theoretical, practical, and ethical implications of these results.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 18 of its 132 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

18 of 132 references.

References · 18

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6 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 4; 2026: 1.

14 papers.

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  1. 2024Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implicationscited by 30
  2. 2024OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language modelscited by 20
  3. 2024Prompt engineering paradigms for medical applications: scoping review and recommendations for better practicescited by 13
  4. 2023Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messagescited by 10
  5. 2024Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysiscited by 9
  6. 2023The effect of source disclosure on evaluation of AI-generated messages: A two-part studycited by 8
  7. 2025Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generationcited by 8
  8. 2025Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communicationcited by 5
  9. 2024InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detectioncited by 4
  10. 2024Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMscited by 3
  11. 2025A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucinationcited by 2
  12. 2025Working with Large Language Models to Enhance Messaging Effectiveness for Vaccine Confidencecited by 1
2 more
  1. 2024GPT-4 Generated Narratives of Life Events using a Structured Narrative Prompt: A Validation Studycited by 0
  2. 2026Structured Exploration vs. Generative Flexibility: A Field Study Comparing Bandit and LLM Architectures for Personalised Health Behaviour Interventionscited by 0