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

Abstract

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.

26 pages including references, 3 figures

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Cited by in corpus (5)

• Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
• OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models
• The effect of source disclosure on evaluation of AI-generated messages: A two-part study
• Artificial social influence via human-embodied AI agent interaction in immersive virtual reality (VR): Effects of similarity-matching during health conversations
• A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination