Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review

TL;DR

This comprehensive review discusses how AI, especially machine learning and deep learning, advances infectious disease prediction, highlights data-related challenges, and assesses AI's potential and limitations in disease management.

Contribution

It categorizes AI applications in infectious disease prediction into three areas and critically evaluates the challenges and potential of AI in this field.

Findings

AI techniques improve disease prediction accuracy

Data type and quality impact AI effectiveness

AI has limitations in real-world disease management

Abstract

Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.