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February 26, 2026Micromachines2 citationsOpen Access

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

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YFYan FengMLMing La

Key Points

  • To summarize machine learning-based biosensing techniques for monitoring COVID-19 and similar infectious diseases.
  • Reviewed machine learning algorithms relevant to biosensing.
  • Discussed machine-learning-assisted biosensors for disease monitoring.
  • Highlighted challenges and future directions in the field.
  • Identified the importance of effective monitoring tools for COVID-19.
  • Noted advancements in biosensor technology using machine learning.
  • Emphasized the potential of AI in developing healthcare monitoring systems.

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.

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Cite This Study

Feng et al. (2026) studied this question.

synapsesocial.com/papers/699fe41d95ddcd3a253e86aehttps://doi.org/10.3390/mi17030283
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