Clinical metagenomic next-generation sequencing (mNGS) is rapidly moving from research to clinical laboratories, transforming how physicians diagnose and treat infectious diseases. This approach analyzes microbial and host genetic material (DNA and RNA) in patient samples, with applications in antimicrobial resistance, the microbiome, human host gene expression, and oncology. Challenges in implementing mNGS include validation, regulatory considerations, and clinical utility. Despite potential, clinical adoption has lagged due to factors such as the complexity of microbial and host interactions, the difficulty in determining whether a detected microorganism is a contaminant, colonizer, or pathogen, and the need for standardized reference materials. mNGS has been used for diagnosing infectious diseases, such as neuroleptospirosis, and for public health applications, including outbreak investigations and antimicrobial resistance surveillance. It enables the identification of all potential pathogens, including bacteria, viruses, fungi, and parasites, and can analyze the human host response through transcriptomics. Applications include microbiome analysis, transcriptomics, and oncology, with potential for precision medicine. However, challenges remain in clinical implementation, including cost, reimbursement, turnaround time, and the need for standardized reference standards. mNGS involves steps such as nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis. Targeted and untargeted approaches are used, with targeted methods focusing on specific pathogens or genes, while untargeted methods sequence all DNA and RNA in a sample. Both approaches have limitations, such as reduced sensitivity in high-background samples, and require methods to enrich for microbial reads. Bioinformatics challenges include the need for user-friendly software, accurate data processing, and validation of results. Clinical implementation of mNGS requires quality management, standardized protocols, and careful handling of samples to avoid contamination. Laboratory workflows must be optimized for high-quality results, with considerations for staff training, sample handling, and data storage. Bioinformatics pipelines involve complex algorithms and require validation to ensure accuracy and reproducibility. Public databases for microbial reference genomes are continuously updated, and laboratories must keep track of exact versions to ensure accurate analysis. Cost considerations include the high per-sample reagent cost, despite reduced sequencing costs. Most laboratories perform library preparation manually, requiring significant staff time. Automation and robotic equipment are needed for high-throughput processing. The clinical laboratory must balance the need for accurate, reproducible results with the practical challenges of implementing mNGS in routine practice. Overall, mNGS holds great promise for improving infectious disease diagnosis and precision medicine, but its successful implementation requires addressing technical, regulatory, and clinical challenges.Clinical metagenomic next-generation sequencing (mNGS) is rapidly moving from research to clinical laboratories, transforming how physicians diagnose and treat infectious diseases. This approach analyzes microbial and host genetic material (DNA and RNA) in patient samples, with applications in antimicrobial resistance, the microbiome, human host gene expression, and oncology. Challenges in implementing mNGS include validation, regulatory considerations, and clinical utility. Despite potential, clinical adoption has lagged due to factors such as the complexity of microbial and host interactions, the difficulty in determining whether a detected microorganism is a contaminant, colonizer, or pathogen, and the need for standardized reference materials. mNGS has been used for diagnosing infectious diseases, such as neuroleptospirosis, and for public health applications, including outbreak investigations and antimicrobial resistance surveillance. It enables the identification of all potential pathogens, including bacteria, viruses, fungi, and parasites, and can analyze the human host response through transcriptomics. Applications include microbiome analysis, transcriptomics, and oncology, with potential for precision medicine. However, challenges remain in clinical implementation, including cost, reimbursement, turnaround time, and the need for standardized reference standards. mNGS involves steps such as nucleic acid extraction, library preparation, sequencing, and bioinformatics analysis. Targeted and untargeted approaches are used, with targeted methods focusing on specific pathogens or genes, while untargeted methods sequence all DNA and RNA in a sample. Both approaches have limitations, such as reduced sensitivity in high-background samples, and require methods to enrich for microbial reads. Bioinformatics challenges include the need for user-friendly software, accurate data processing, and validation of results. Clinical implementation of mNGS requires quality management, standardized protocols, and careful handling of samples to avoid contamination. Laboratory workflows must be optimized for high-quality results, with considerations for staff training, sample handling, and data storage. Bioinformatics pipelines involve complex algorithms and require validation to ensure accuracy and reproducibility. Public databases for microbial reference genomes are continuously updated, and laboratories must keep track of exact versions to ensure accurate analysis. Cost considerations include the high per-sample reagent cost, despite reduced sequencing costs. Most laboratories perform library preparation manually, requiring significant staff time. Automation and robotic equipment are needed for high-throughput processing. The clinical laboratory must balance the need for accurate, reproducible results with the practical challenges of implementing mNGS in routine practice. Overall, mNGS holds great promise for improving infectious disease diagnosis and precision medicine, but its successful implementation requires addressing technical, regulatory, and clinical challenges.