Abstract

This paper explores the transformative role of artificial intelligence (AI) in patient risk stratification within public health, emphasizing its potential to improve preventive care outcomes through predictive analytics. AI technologies, particularly machine learning models, enable healthcare systems to predict patient health risks, enhance diagnostic accuracy, and optimize resource allocation. By analyzing vast amounts of patient data, AI can identify high-risk individuals for chronic diseases, mental health conditions, and other health crises, allowing for timely and targeted interventions. Case studies are presented to illustrate AI’s effectiveness in early disease detection, mental health risk identification, and large-scale population health management. Furthermore, the integration of AI in healthcare is shown to contribute to cost-effectiveness by reducing hospital readmissions, streamlining workflows, and preventing the progression of preventable diseases. Ethical and regulatory considerations are discussed, addressing concerns such as data privacy, algorithmic bias, and transparency. Future directions for AI in public health, including the integration with emerging technologies and the development of explainable models, are also explored. Finally, policy implications are offered, advocating for frameworks to ensure the ethical use of AI while supporting research and workforce development to maximize AI’s impact in improving healthcare outcomes.