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In clinical diagnostics, mNGS enables simultaneous detection of diverse pathogens without prior hypothesis, though its yield depends heavily on specimen type and clinical context. In public health, mNGS has demonstrated remarkable utility in outbreak tracing, novel pathogen discovery, antimicrobial resistance (AMR) surveillance, and One Health initiatives. However, massive data volumes pose persistent challenges in bioinformatics, standardization, and computational demands. Future integration of artificial intelligence, automated platforms, and multi-omics approaches will enhance the conversion of raw data into actionable insights. 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