Underpinned by advancements in genome sequencing technologies, the contemporary era of precision medicine is characterized by a paradigm shift away from empirical dosing and toward therapeutic selection that is informed by science. By addressing intricate structural differences and the “dark genome,” which short-read techniques sometimes overlook, TGS or LRS is also developing to supplement NGS. Genomics has evolved from a sequencing problem to an interpretation problem as a result of the ensuing exponential expansion in data, which calls for the combination of artificial intelligence and machine learning for quick and precise interpretation. Scaling precision medicine increasingly requires AI/ML, but Explainable Al is still essential for clinical confidence. A major component of this change is pharmacogenomics, which rationalizes pharmacological therapy by optimizing dosage and reducing adverse drug reactions through the use of genetic information.

Pre-emptive testing for important genes like CYP2C19 and VKORC1 is becoming more popular in oncology due to authoritative evidence-based guidance from groups like the Clinical Pharmacogenetics Implementation Consortium Standard genome profiling finds actionable biomarkers that direct tailored treatments for diseases such as non- small cell lung cancer. Furthermore, dynamic, real-time monitoring of tumor development and resistance is made possible via liquid biopsy. WGS is lowering the “diagnostic odyssey” in uncommon diseases, which frequently results in quantifiable adjustments to clinical care, such as medication repurposing. The broad use of genomics is impeded by fragmented reimbursement policies and substantial Ethical, Legal, and Social Implications, especially with regard to the handling of incidental findings and data privacy under current laws such as GINA, despite the overwhelming evidence of its clinical and economic utility. Achieving a future of proactive, predictive, and individualized care requires overcoming these translational obstacles.

Keywords: Whole Genome Sequencing, Artificial intelligence, Machine learning, Explainable AI, Next Generation Sequencing