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Emerging viral outbreaks, such as the COVID-19 pandemic, have highlighted the critical need for rapid, accurate, and scalable virus detection systems. This review aims to explore the integration of artificial intelligence (AI) and nanotechnology as a transformative approach for real-time virus prediction, monitoring, and management. The review systematically analyzes how machine learning (ML) and deep learning (DL) algorithms are being applied to identify viral mutations, forecast outbreak trajectories, and analyze complex virological data. It also highlights recent advances in nanotechnology, including the development of nanosensors, nanoparticle-based diagnostics, and lab-on-chip devices. The synergy between AI and nanotechnology is examined through selected case studies and near-real-world implementation efforts. The convergence of AI and nanotechnology represents a promising translational pipeline toward highly sensitive, rapid, and personalized viral detection systems, with substantial clinical validation and regulatory maturation still required before routine deployment. When combined, AI enhances the interpretability and responsiveness of nanotech-based diagnostics, while nanodevices provide high-resolution data for AI-driven prediction models. This integration supports more adaptive, data-driven public health responses. This review presents an up-to-date, interdisciplinary overview of AI-nanotech integration in virology. It identifies current challenges such as data privacy, algorithmic bias, and regulatory barriers, while proposing future directions for personalized and globally inclusive virus surveillance systems. The combined power of biological insight and technological innovation outlines an emerging paradigm for managing viral threats, contingent upon continued translational validation and real-world implementation.
Garg et al. (Wed,) studied this question.