Next-generation viral detection through AI-enhanced nanotechnology: advances, challenges, and future directions

Area of Science:

• Virology
• Nanotechnology
• Artificial Intelligence

Background:

• Emerging viral outbreaks necessitate advanced detection systems.
• The COVID-19 pandemic underscored the need for rapid, accurate, and scalable virus detection.
• Existing methods require enhancement for real-time monitoring and prediction.

Purpose of the Study:

• To explore the integration of artificial intelligence (AI) and nanotechnology for real-time virus detection, prediction, and management.
• To analyze the application of machine learning (ML) and deep learning (DL) in virology.
• To highlight advances in nanotechnology for diagnostics.

Main Methods:

• Systematic review of AI algorithms (ML/DL) for viral mutation identification and outbreak forecasting.
• Analysis of nanotechnology advancements, including nanosensors and lab-on-chip devices.
• Examination of AI-nanotechnology synergy through case studies and implementation efforts.

Main Results:

• AI enhances nanodiagnostics' interpretability and responsiveness.
• Nanodevices provide high-resolution data for AI prediction models.
• Integration supports adaptive, data-driven public health responses for viral threat management.

Conclusions:

• The convergence of AI and nanotechnology offers a promising pipeline for sensitive, rapid, and personalized viral detection.
• Substantial clinical validation and regulatory progress are needed for routine deployment.
• Addressing challenges like data privacy and algorithmic bias is crucial for inclusive global surveillance.