AI-driven health data analytics for early detection of infectious diseases: A conceptual exploration of U.S. public health strategies

This review paper explores the role of AI-driven health data analytics in enhancing the early detection and response to infectious diseases within U.S. public health strategies. It begins by outlining the importance of timely detection in controlling outbreaks and discusses current public health systems and their limitations, including passive surveillance and data fragmentation. The paper highlights innovative AI technologies such as machine learning, natural language processing, and predictive modeling, which can significantly improve disease monitoring and forecasting. By analyzing vast datasets in real time, these technologies facilitate quicker identification of emerging health threats and enable tailored public health responses. Furthermore, the review presents recommendations for integrating AI innovations into existing public health strategies, emphasizing improved data interoperability, investment in infrastructure, stakeholder collaboration, community engagement, and real-world pilot programs. Ultimately, the findings underscore the potential of AI-driven health data analytics to transform infectious disease management, thereby safeguarding public health.