Artificial Intelligence in Infectious Disease Detection and Control: A Systematic Literature Review of Advancements, Challenges, and Future Directions

Background: Artificial Intelligence (AI) plays a critical role in combating infectious diseases by enabling early detection, outbreak prediction, and real-time surveillance. This study explores how AI improves prediction accuracy, responsiveness, and overall public health outcomes. Method: A systematic review of literature from 2019 to 2025 was conducted across databases including PubMed, Scopus, IEEE Xplore, and Google Scholar. Selected studies focused on machine learning, deep learning, and neural networks applied to infectious disease management, particularly in predicting outbreaks and guiding real-time responses. Results: A total of 30 peer-reviewed studies were reviewed. The findings indicate that AI models significantly enhance the early detection and forecasting of infectious diseases, including COVID-19, malaria, and Ebola. Compared to traditional methods, AI demonstrated greater predictive accuracy and faster response capabilities. Real-time AI-powered surveillance also supported better resource allocation and outbreak management. The effectiveness of these models varied based on disease type, data quality, and local health infrastructure. Conclusion: AI has proven effective in enhancing infectious disease control through improved prediction, faster response, and better-informed decision-making. These findings underscore the importance of integrating AI tools into public health infrastructure while addressing persistent challenges such as data standardization, ethical concerns, and technological access disparities.