AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery

Emerging viral outbreaks continue to pose a persistent global health threat, underscoring the urgent need for a shift from reactive to proactive health security strategies. Viral metagenomic next-generation sequencing (mNGS) offers an unbiased, powerful approach to pathogen detection and discovery, yet its utility has been constrained by the computational complexity and slow turnaround time of data analysis during outbreak crises. The integration of artificial intelligence (AI) and mNGS is dismantling these barriers, enabling faster, more scalable outbreak response. This review synthesizes how AI-driven analytics are transforming mNGS applications, from genome assembly to sequence classification, using advanced architectures such as convolutional neural networks, recurrent neural networks, and transformers. Beyond accelerating workflows, AI's capacity for pattern recognition outperforms traditional homology-based methods, facilitating the discovery of novel viral families and tracing hidden transmission chains through anomaly detection. Nonetheless, critical challenges remain, including limited training data, the interpretability of AI models, and resource-intensive computational demands that risk widening an "AI divide" in global health. We evaluate these obstacles and highlight forward-looking strategies, including federated learning for privacy-preserving data sharing and explainable AI for improving trust and biological insight. Looking ahead, we envision an "AI-first" paradigm for outbreak preparedness, anchored in integrated "Digital Immune Systems" for continuous, global-scale surveillance. By framing the synergy between mNGS and AI as a transformative leap, this review underscores its potential to strengthen resilience against future pandemics.