The Role and Limitations of Artificial Intelligence in Combating Infectious Disease Outbreaks

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in the management of pandemics, significantly enhancing disease prediction, diagnostics, drug discovery, and vaccine development. This manuscript explores AI's multifaceted applications during infectious disease outbreaks, from predictive modeling and outbreak forecasting to the acceleration of vaccine development and antimicrobial resistance detection. AI-driven technologies, including deep learning and reinforcement learning, have shown remarkable effectiveness in improving diagnostic accuracy, streamlining drug discovery processes, and providing real-time decision-making support for healthcare providers. However, despite its substantial contributions, the deployment of AI in pandemic management faces key limitations, including concerns about data privacy, model transparency, and the need for constant updates to adapt to emerging pathogens. The integration of AI with human expertise is essential to optimize global health outcomes and address these challenges. This review highlights both the potential and the obstacles to fully leveraging AI in pandemic response, proposing pathways for overcoming current limitations and maximizing AI's impact on future outbreaks.

Keywords: artificial intelligence; deep learning; infectious diseases; machine learning; neural networks; outbreak; pandemic; reinforcement learning.