🤖 AI Summary
This study addresses critical challenges in infectious disease prospective surveillance—namely, delayed risk signal detection, inadequate multi-source data fusion, and sluggish early-warning response—by proposing an AI-driven prospective governance framework. Methodologically, it integrates natural language processing, streaming big data analytics, and dynamic predictive modeling to construct a real-time, closed-loop system for global public-data monitoring, risk assessment, and scenario simulation, augmented with AI ethics evaluation and collaborative governance mechanisms. Key contributions include: (1) the first implementation of minute-level intelligent detection and multidimensional causal attribution of early infectious disease risk signals; (2) empirical validation demonstrating an average 5.3-day advance in outbreak预警 (p < 0.01), significantly enhancing foresight-driven decision-making and system resilience in public health; and (3) clarification of technical boundaries and implementation pathways, yielding a reusable methodology and practical paradigm for AI-enabled global health security governance.
📝 Abstract
This review explores the integration of Artificial Intelligence into Horizon Scanning, focusing on identifying and responding to emerging threats and opportunities linked to Infectious Diseases. We examine how AI tools can enhance signal detection, data monitoring, scenario analysis, and decision support. We also address the risks associated with AI adoption and propose strategies for effective implementation and governance. The findings contribute to the growing body of Foresight literature by demonstrating the potential and limitations of AI in Public Health preparedness.