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This scoping review maps the application of large language models for public health communication, indicating a need for better misinformation correction efforts.
This scoping review maps 552 studies applying large language models (LLMs) to population-level public health communication, following JBI methodology and PRISMA-ScR guidelines. Six databases and grey literature sources were searched (2019-2026). AI-assisted screening was validated against human review (Cohen's kappa = 0.83). Misinformation detection dominated (n = 186, 33.7%), yet misinformation correction was nearly absent (n = 11)---a 17:1 ratio. Only 9.6% of tools were deployed in practice, 83% of studies targeted English only, and 76% discussed no ethical considerations. The field has invested heavily in identifying health misinformation but almost nothing in responding to it. Redirecting effort toward correction, deployment, multilingual development, and non-communicable disease applications is essential.
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Hayden Farquhar (2026) studied this question.
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