Artificial Intelligence And Big Data For Enhancing Public Health Surveillance And Disease Prevention: A Systematic Review

Abstract

This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.