Journal of Innovative Science

Abstract: Artificial intelligence (AI) is increasingly recognized as a transformative force in public health, offering new methods for disease surveillance, early outbreak detection, precision prevention, and equitable resource allocation. This paper examines the opportunities and challenges of AI-driven optimization of public health interventions in the United States. We highlight applications that have improved prediction accuracy in infectious disease forecasting and personalized strategies for chronic disease prevention. Benefits include scalability, cost-effectiveness, and the integration of multimodal data sources, such as electronic health records, environmental datasets, and wearable technologies. Despite these advances, critical limitations persist. Issues of algorithmic bias, data representativeness, transparency, and infrastructure gaps continue to limit generalizability and trust in AI-powered systems. Ethical concerns, particularly surrounding privacy, equity, and accountability, demand robust governance frameworks and community engagement. Future directions emphasize the development of explainable and adaptive models, investment in workforce training, and stronger regulatory oversight to balance innovation with fairness. AI’s success in public health will ultimately be measured not by technical sophistication but by its capacity to improve health outcomes equitably across diverse U.S. populations.