# Overview in Machine-Learning-Assisted Sensing Techniques for Monitoring COVID-19

## TL;DR

This paper reviews how machine learning helps in creating biosensors for monitoring and managing diseases like COVID-19.

## Contribution

The paper provides a comprehensive overview of machine-learning-assisted biosensors for infectious disease monitoring.

## Key findings

- Machine learning algorithms enhance the accuracy of biosensors for disease detection.
- Current challenges include improving sensor reliability and scalability for real-world applications.
- Future perspectives focus on integrating AI for better healthcare monitoring and disease management.

## Abstract

Viruses suddenly emerging from obscurity or anonymity affect our quality of life and increase incidence rate and mortality. A typical example is the global coronavirus disease 2019 (COVID-19) pandemic. Although severe acute respiratory syndrome coronavirus 2, known as the pathogen of COVID-19 has been significantly eliminated, its monitoring is still crucial, as the infectious disease may break out again. Therefore, it is necessary to develop simple and effective tools for monitoring COVID-19 and other diseases. Here, we summarize the progress of machine-learning-based biosensors in the monitoring and management of COVID-19. This article mainly includes three sections: machine learning algorithms, machine-learning-assisted biosensors, and challenges and future perspectives. We believe that this work is valuable for developing artificial-intelligence-based innovative analytical devices for healthcare monitoring and management of COVID-19 and other infectious diseases.