Artificial Intelligence in Pandemic Preparedness and Response: A Systematic Review of Applications, Opportunities, and Challenges

Abstract

Abstract

The COVID-19 pandemic exposed significant weaknesses in global healthcare systems, highlighting the need for innovative approaches to strengthen preparedness and response efforts. Artificial Intelligence (AI) has emerged as a promising tool for enhancing disease surveillance, forecasting, diagnostics, resource allocation, and public health decision-making. This systematic review evaluated the role of AI in pandemic preparedness and response and identified factors influencing its implementation. A comprehensive search of PubMed, Scopus, IEEE Xplore, and Google Scholar was conducted for studies published between January 2010 and March 2024. Thirty-four studies met the inclusion criteria. AI applications were identified across healthcare resource forecasting, vaccine distribution, risk stratification, diagnostic imaging, surveillance systems, and decision support. Machine learning models improved prediction of healthcare demand and disease trends, while deep learning approaches enhanced diagnostic workflows. Key challenges included data privacy concerns, algorithmic bias, infrastructure limitations, and governance issues. AI has significant potential to strengthen pandemic preparedness and response; however, it should be viewed as a supportive tool rather than a replacement for human expertise and clinical judgment.