Artificial Intelligence in Public Health: Optimizing Clinical Triage and Preventing Infectious Disease Outbreaks

Artificial intelligence (AI) is increasingly transforming public health from a predominantly reactive model of treating established disease towards proactive detection, prediction, and prevention. Within hospitals, natural language processing and machine learning can analyse electronic health records, clinical observations, and symptom patterns to support triage and identify patients at increased risk of infectious disease. At population level, AI-driven early warning systems can combine epidemiological reports, mobility data, environmental information, and other signals to identify emerging outbreaks before conventional surveillance systems respond. Consumer wearable devices may provide an additional source of continuous physiological data, including changes in resting heart rate, sleep, and activity that can precede detectable symptoms. However, implementation remains constrained by fragmented datasets, algorithmic bias, limited explainability, privacy risks, and unequal access to digital technologies. Effective deployment therefore requires rigorous validation, transparent governance, and continuous human oversight. AI should augment clinical and public health expertise rather than replace it.