AI-Enabled Public Health Surveillance: Interim Findings from a Scoping Review of Governance and Implementation Frameworks.

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.