UC San Diego

Health misinformation on social media is not only a problem of inaccurate content, but also a communication problem shaped by trust, uncertainty, emotion, identity, platform design, and unequal access to credible information. Artificial intelligence (AI) is increasingly proposed as a tool for addressing this problem, yet much of the literature emphasizes detection accuracy, automated moderation, or technical performance. This mini review reframes AI as part of a broader health communication response cycle for addressing health misinformation on social media. Drawing on recent literature, it examines how AI can support social listening, intervention design and delivery, and how these systems are evaluated. AI-enabled social listening can help identify emerging misinformation narratives, information voids, audience concerns, emotional responses, and diffusion patterns. These insights may inform corrective messages, prebunking content, plain-language explanations, multilingual communication, chatbot-based engagement, and audience-tailored responses. It should be noted that the current evidence base remains concentrated in COVID-19 and vaccine-related contexts, with more limited evidence from other health domains. Moreover, existing studies often evaluate technical performance rather than real-world communication outcomes, and evidence for AI-enabled interventions remains mixed and insufficient. Overall, AI has potential to support health communication against misinformation on social media, but it should not replace health communicators, public health professionals, clinicians, or trusted community messengers. Instead, its value depends on transparent, human-in-the-loop, theory-informed, and community-engaged implementation.