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AI-Enabled Public Health Surveillance: Interim Findings from a Scoping Review of Governance and Implementation
Erika Ito1, Leroy Harris2, Sigfried Gold2,3
1Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
None:
Artificial intelligence (AI)-enabled public health surveillance systems have expanded rapidly following the COVID-19 pandemic, yet governance and implementation structures guiding their deployment remain inconsistently defined. We conducted a scoping review following PRISMA-ScR guidelines to examine governance and regulatory frameworks associated with AI-enabled surveillance systems. A search of three databases yielded 707 unique records after deduplication. Preliminary findings indicate a predominance of centralized governance models and limited explicit articulation of equity safeguards or public oversight mechanisms. Results will provide a typology of centralized, federated, and hybrid governance models characterized by authorization structure, data governance architecture, oversight mechanisms, and equity safeguards, to inform the design of accountable AI surveillance systems.
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