Generative AI and health misinformation: production, propagation, and mitigation-a systematic review

BACKGROUND: Generative artificial intelligence technologies have disrupted information ecosystems, posing new threats to public health by enabling rapid, scalable manufacture of convincing but false health stories. This systematic review synthesizes evidence on how generative AI reconfigures health misinformation creation, dissemination, and moderation.
METHODS: In line with PRISMA 2020, 15 empirical studies published between January 2023 and August 2025 were included. Databases consulted were MEDLINE (via PubMed), Embase, Scopus, Web of Science Core Collection, ACM Digital Library, IEEE Xplore, PsycINFO, Communication & Mass Media Complete, arXiv, and medRxiv/SSRN. Studies were contrasted on the basis of production capacity, propagation dynamics, and efficacy of mitigation at technical, sociotechnical, and governance layers.
RESULTS: The synthesis indicates that generative AI substantially increases the volume, speed, and perceived credibility of health disinformation production, while altering its propagation dynamics. Users often struggle to distinguish AI‑generated from human‑authored health misinformation, and their sharing intentions are not tightly coupled with perceived accuracy. Existing detection systems show limited performance against AI‑generated content, and while labeling interventions can reduce perceived accuracy, their effects are context‑dependent.
CONCLUSION: Generative AI transforms the health misinformation landscape by lowering barriers to creation and exploiting platform and behavioral dynamics. Current mitigation strategies-spanning technical, sociotechnical, and governance layers-are promising but remain nascent and unevenly evaluated. Future work must prioritize multimodal, multilingual, and health‑specific verification, as well as real‑world testing of interventions, to build equitable and resilient health information ecosystems.