The Future of Public Health: Integrating Artificial Intelligence in Disease Surveillance and Prevention

Abstract

This study examines the role of artificial intelligence in disease surveillance and prevention, highlighting its applications, benefits, challenges, and future implications in public health strategies. A descriptive analysis method was employed to review recent advancements in AI-driven disease surveillance and prevention. Peer-reviewed articles, government reports, and public health databases from 2020 to 2025 were analyzed to assess AI applications, predictive modeling techniques, and ethical considerations. Key themes explored included AI-driven outbreak prediction, real-time data analytics, wearable health monitoring, natural language processing in public health, and AI-enhanced vaccination strategies. The study also evaluated the challenges related to data privacy, algorithmic bias, regulatory frameworks, and AI integration with existing health infrastructure. The findings indicate that AI has significantly improved the efficiency, accuracy, and scalability of disease surveillance by automating data collection, enhancing outbreak prediction, and enabling real-time health monitoring. AI-driven predictive models have successfully identified emerging health threats, optimized resource allocation, and strengthened public health response mechanisms. However, concerns related to data privacy, ethical AI deployment, and regulatory oversight remain critical barriers to widespread AI adoption in public health. Additionally, challenges in algorithmic fairness and the integration of AI with traditional epidemiological frameworks require further attention to ensure equitable healthcare outcomes. Artificial intelligence presents transformative opportunities for improving public health surveillance and disease prevention through predictive analytics, real-time monitoring, and automated decision-making. However, ethical, regulatory, and infrastructural challenges must be addressed to maximize AI’s potential while ensuring responsible deployment. Future research should focus on enhancing AI fairness, transparency, and governance to support sustainable and equitable public health strategies.