AI-Empowered Viral Metagenomics for Clinical Diagnosis: Advances, Bottlenecks, and Translational Pathways

Abstract

Viral metagenomics, leveraging high-throughput sequencing technologies, provides comprehensive, hypothesis-free characterization of viral communities in clinical specimens, establishing itself as a pivotal tool for clinical diagnosis, pathogen discovery, and epidemiological surveillance of viral infectious diseases. The integration of artificial intelligence (AI) has demonstrated transformative potential in viral metagenomic data analysis, significantly enhancing sequence classification, feature extraction, pattern recognition, and result interpretation, thereby improving analytical efficiency, accuracy, and automation. Machine-learning and deep-learning approaches have shown potential for detecting low-abundance viral signals and mitigating background noise in complex clinical samples, and facilitating pathogen identification in complex clinical samples, substantially augmenting the clinical utility of viral metagenomics. Nevertheless, routine clinical implementation faces persistent challenges, including high host nucleic acid background, low viral titers, contamination control, limited reference databases, absence of standardized analytical pipelines, and limited interpretability and generalizability of some predictive models. Furthermore, clinical validation, result reproducibility, ethical compliance, and data security represent critical translational barriers that impede widespread adoption. This review comprehensively summarizes recent advances and clinical applications of AI-empowered viral metagenomics, highlighting its transformative potential in pathogen detection, diagnosis of challenging infections, infectious disease surveillance, and precision medicine. We comprehensively address major technical bottlenecks and translational barriers that must be overcome to facilitate standardized development and clinical implementation of this emerging paradigm.