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Evaluating AI-Mediated Health Communication via Large Language Model-Based Frequently Asked Questions Rewriting to
Ching-Hua Chuan1, Jiajing Tang1, Zixiao Yang2
1Department of Interactive Media, School of Communication, University of Miami, 5100 Brunson Drive, Coral Gables, FL, 33146, United States, 1 3052845045.
JMIR AI
|May 12, 2026
Summary
Large language models (LLMs) can improve clinical trial communication by rewriting frequently asked questions (FAQs). AI-generated content enhances public attitudes and intentions toward clinical trial participation, especially for underrepresented groups.
Area of Science:
- Health communication
- Artificial intelligence in healthcare
- Clinical trial recruitment
Background:
- Effective communication is crucial for clinical trial enrollment, yet recruitment challenges persist, particularly among older adults, minority populations, and those with limited health literacy.
- Large language models (LLMs) show potential for generating health information, but their impact on public attitudes towards clinical trials is not well understood.
Purpose of the Study:
- To compare standard clinical trial FAQs with LLM-generated versions to assess improvements in public attitudes and intentions.
- To identify the mechanisms through which AI-generated content influences engagement with clinical research.
Main Methods:
- Collected 308 question-answer pairs from 38 health organizations, selecting 11 frequent types for analysis.
- Conducted a comparative survey experiment with 440 participants randomly assigned to view either original or GPT-4o-generated FAQ content.
- Analyzed changes in attitudes toward clinical trials and examined Theory of Planned Behavior constructs to understand moderating factors.
Main Results:
- Participants viewing GPT-4o-generated FAQs showed a marginally greater positive change in outcome evaluation attitudes compared to those viewing standard FAQs (P=.05).
- Individual factors such as age, race, risk aversion, and fear of ineffective or unknown treatments significantly moderated the impact of AI-generated content on attitude change.
- AI-generated responses demonstrated a positive effect on attitudes, particularly for older adults and Black participants.
Conclusions:
- This study is the first to apply the Theory of Planned Behavior to evaluate LLM-rewritten clinical trial FAQs.
- GPT-4o-generated responses improved attitudes toward clinical trials among underrepresented groups, linking to increased participation intentions.
- AI-generated language can enhance public perceptions and engagement with clinical research, addressing communication barriers.
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