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

Area of Science:

• Biomedical Engineering
• Infectious Disease Surveillance
• Artificial Intelligence in Healthcare

Background:

• Emerging viral threats, exemplified by the COVID-19 pandemic, necessitate robust monitoring systems.
• Continued vigilance for pathogens like SARS-CoV-2 is crucial despite current control measures.
• Simple, effective tools are needed for real-time disease detection and management.

Purpose of the Study:

• To review advancements in machine learning-based biosensors for COVID-19 monitoring and management.
• To explore the integration of artificial intelligence (AI) in analytical devices for infectious diseases.
• To highlight the potential of AI-driven biosensors in public health surveillance.

Main Methods:

• Review of machine learning algorithms applicable to biosensing.
• Analysis of machine learning-assisted biosensor designs and applications for COVID-19.
• Discussion of challenges and future perspectives in AI-based healthcare monitoring.

Main Results:

• Machine learning algorithms enhance biosensor sensitivity and specificity.
• AI-powered biosensors show promise for rapid and accurate detection of viral pathogens.
• Integration of AI facilitates personalized healthcare and disease management strategies.

Conclusions:

• Machine learning-based biosensors represent a significant advancement in infectious disease monitoring.
• AI-driven analytical devices offer innovative solutions for healthcare, particularly for emerging infectious diseases.
• Further research and development are essential to overcome challenges and realize the full potential of these technologies.