Machine learning in genomic diagnostics for precision medicine

The integration of machine learning (ML) in genomic diagnostics has revolutionized precision medicine, fundamentally transforming how genetic variations are identified, interpreted, and utilized in clinical settings. The article examines the current state, implementation challenges, and future directions of ML applications in genomic medicine. With the global market for AI in genomics projected to reach billions by future years, growing at a significant CAGR, the field is experiencing rapid advancement. Modern ML algorithms demonstrate unprecedented accuracy, achieving high accuracy in pathogenic variant identification, while processing capabilities have expanded to handle large volumes of genomic data annually. The implementation of distributed computing frameworks has enabled substantial processing rates of genomic data per hour, while maintaining excellent accuracy in variant identification. The article discusses the evolution of data processing pipelines, challenges in data quality and standardization, ethical considerations including privacy protection, and emerging technologies in multi-modal learning systems. The article reveals that ML-based approaches have reduced diagnostic times from weeks to hours, improved rare disease diagnosis rates significantly, and achieved impressive accuracy in identifying driver mutations across multiple cancer types. These advancements suggest a promising future for ML-driven precision medicine, despite existing challenges in data diversity and standardization.