A forward-looking blueprint calling for a shift from a traditional, post-event-focused infectious disease management system to a proactive forecasting and preemptive public health response system using artificial intelligence (AI) and big data was presented by researchers at Jeonbuk National University (JBNU).

Professor Jae-Hyuk Cho of the Department of Software Engineering at JBNU (specializing in artificial intelligence) delivered a keynote presentation titled "Predicting and Responding to Infectious Diseases Using AI" on the 10th at the 4th Public–Private–Academic Infectious Disease Expert Symposium, hosted by the Daejeon Metropolitan City Infectious Disease Control Support Group. He proposed data-driven scientific disease-control solutions to prepare for future infectious disease crises.

At the symposium, Professor Cho identified the greatest current challenge in infectious disease response as the fragmentation of data. Clinical data from medical institutions and environmental and public data are dispersed across different organizations, making rapid, integrated analysis difficult. To overcome this limitation, he proposed building a "data-driven proactive management system" that organically links scattered data to detect infectious disease risks in advance.

As a concrete method, Professor Cho introduced a case example of building an "Integrated Management System for Linking Infectious Disease Patient Information." He explained that if hospital electronic medical record (EMR) data are linked with weather and other environmental data and public health data in an integrated network, AI can enable real-time monitoring of infectious disease trends, analyze patient severity, predict bed demand in emergency departments and intensive care units, and analyze medical equipment utilization all at once. This would allow medical staff and public health authorities to make optimized decisions within the golden time.

Professor Cho emphasized, "The success or failure of infectious disease response depends on how quickly risk signs are detected and how accurately resources are allocated. AI and data analytics are the most powerful tools to ensure both that accuracy and speed."

He added that essential conditions for the technology to be implemented in the field include standardization of data collection systems, information-sharing agreements among relevant agencies, and active design participation by frontline practitioners. He expressed willingness to engage in close collaboration among industry, academia, research institutes, and regional infectious disease management agencies such as the Daejeon Metropolitan City authorities.

Meanwhile, Professor Jae-Hyuk Cho's research team at JBNU is actively conducting major related research projects, including refinement of infectious disease prediction models, development of a multi-institution EMR linkage platform, and automation of AI-based epidemiological analysis. The university expects that this symposium presentation will serve as a practical launch for cooperation to apply and validate the university's excellent AI research achievements in local government disease-control operations.