Addressing the Spread of Infectious Diseases in the Era of Explainable AI

Abstract

Artificial intelligence technology (AI) has significantly advanced the field of epidemiology. During COVID-19 pandemic, multiple researches have employed machine learning (ML) and deep learning (DL) in the early detection, monitoring, future outbreaks prediction and drugs and vaccines development. But the real implementation of AI in healthcare institutions still very limited except for few applications such as screening body temperature using thermal cameras at the entrances of public places. The reason behind this limitation is the “black-box” nature of AI algorithms. Explainable AI (XAI) addresses this issue by explaining the decisions made by the algorithm. In this review, we introduce mathematical epidemiology and summarize the history and subfields of AI in addition to its applications in fighting pandemics. Furthermore, we highlight the added value of XAI in predicting and managing infectious diseases.