Generative AI and health misinformation: a lifecycle-based communication framework
- 1epartment of Communication, University of Maryland, College Park, MD, United States
Abstract
BackgroundGenerative artificial intelligence (AI) is increasingly embedded in the contemporary health information environment, where it can both generate and mitigate misinformation.ObjectiveThis review develops a Lifecycle-Based Framework to provide an integrative account of how generative AI operates across the health misinformation lifecycle from misinformation production and detection to correction and impact.MethodsA narrative integrative review was conducted, drawing on research from communication science, public health, human-computer interaction, and artificial intelligence.ResultsThe review identifies domain-specific mechanisms through which generative AI operates across the health misinformation lifecycle. In production, AI enables scalable generation of fluent, expert-styled content through mechanisms including hallucination, amplification of falsehoods, adversarial vulnerability, and deepfakes. In detection, it supports automated fact-checking, pattern recognition, and classification, while remaining vulnerable to false positives, false negatives, bias, and adversarial adaptation. In correction, generative AI facilitates rebuttals, tailored messaging, conversational engagement, and AI-assisted inoculation, but also raises concerns about trust displacement, epistemic authority, and accountability. At the impact stage, AI-mediated misinformation can influence individual cognition and behavior as well as broader social and institutional outcomes, with fluency, narrative coherence, and personalization serving as important persuasive mechanisms. The framework further conceptualizes the lifecycle as recursive and adaptive, such that production, detection, correction, and impact continually shape one another.ConclusionGenerative AI should be understood not as a single source of misinformation risk, but as a multifunctional component of a dynamic health communication system. Addressing AI-mediated health misinformation therefore requires coordinated, multilevel strategies that account for its simultaneous roles in producing, detecting, correcting, and shaping the consequences of misinformation, while preserving human oversight, institutional accountability, and public trust.
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BibTeX
@article{nan2026generative,
title = {{Generative AI and health misinformation: a lifecycle-based communication framework}},
author = {Xiaoli Nan},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1801842},
url = {https://doi.org/10.3389/fmed.2026.1801842},
note = {CC BY},
}
RIS
TY - JOUR
TI - Generative AI and health misinformation: a lifecycle-based communication framework
AU - Xiaoli Nan
PY - 2026
DA - 2026/10/23
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1801842
UR - https://doi.org/10.3389/fmed.2026.1801842
AB - BackgroundGenerative artificial intelligence (AI) is increasingly embedded in the contemporary health information environment, where it can both generate and mitigate misinformation.ObjectiveThis review develops a Lifecycle-Based Framework to provide an integrative account of how generative AI operates across the health misinformation lifecycle from misinformation production and detection to correction and impact.MethodsA narrative integrative review was conducted, drawing on research from communication science, public health, human-computer interaction, and artificial intelligence.ResultsThe review identifies domain-specific mechanisms through which generative AI operates across the health misinformation lifecycle. In production, AI enables scalable generation of fluent, expert-styled content through mechanisms including hallucination, amplification of falsehoods, adversarial vulnerability, and deepfakes. In detection, it supports automated fact-checking, pattern recognition, and classification, while remaining vulnerable to false positives, false negatives, bias, and adversarial adaptation. In correction, generative AI facilitates rebuttals, tailored messaging, conversational engagement, and AI-assisted inoculation, but also raises concerns about trust displacement, epistemic authority, and accountability. At the impact stage, AI-mediated misinformation can influence individual cognition and behavior as well as broader social and institutional outcomes, with fluency, narrative coherence, and personalization serving as important persuasive mechanisms. The framework further conceptualizes the lifecycle as recursive and adaptive, such that production, detection, correction, and impact continually shape one another.ConclusionGenerative AI should be understood not as a single source of misinformation risk, but as a multifunctional component of a dynamic health communication system. Addressing AI-mediated health misinformation therefore requires coordinated, multilevel strategies that account for its simultaneous roles in producing, detecting, correcting, and shaping the consequences of misinformation, while preserving human oversight, institutional accountability, and public trust.
SN - 2296-858X
ER -