Advances in genomic sequencing have revolutionized personalized medicine by enabling the detailed analysis of an individual's genetic makeup. This progress has significantly lowered the cost and increased the speed of sequencing, making it feasible for widespread clinical application. For instance, the cost of sequencing a whole genome has dropped to around $1,000, which is less than many routine medical tests1. These technological advancements allow for the identification of genetic variants associated with diseases, facilitating early diagnosis and the development of targeted therapies23. The integration of whole-exome sequencing (WES) and whole-genome sequencing (WGS) into clinical practice is expected to transform healthcare by providing precise diagnoses and personalized treatment plans34.

Pharmacogenomics delves into how genetic variations influence individual responses to medications, paving the way for personalized drug therapies that enhance efficacy and minimize adverse effects. By examining gene variants, healthcare providers can tailor drug selection and dosing to the patient's genetic profile, significantly reducing the trial-and-error approach traditionally used in prescribing medications12. This personalized approach is particularly beneficial in managing conditions like cancer, where pharmacogenomics can guide the use of targeted therapies, improving treatment outcomes and patient safety34.

Cancer treatment has significantly benefited from personalized medicine, where genomic sequencing identifies specific genetic mutations driving tumor growth. This precision allows for the development of targeted therapies that are more effective and have fewer side effects compared to traditional treatments12. Liquid biopsies, which analyze circulating tumor DNA (ctDNA) in blood, offer a non-invasive method to diagnose cancer and monitor treatment response in real-time, providing crucial insights for adjusting therapy as needed34.

CRISPR/Cas9, a groundbreaking gene-editing tool, holds immense potential for personalized medicine by enabling precise modifications of genes associated with specific diseases. This technology allows for the direct targeting and correction of genetic mutations, which can lead to more effective treatments for conditions such as cancer and inherited disorders12. For instance, CRISPR/Cas9 can be used to edit tumor genes in cancer cells, potentially eliminating genetic changes that drive tumor growth and metastasis34. As research progresses, the integration of CRISPR/Cas9 with next-generation sequencing (NGS) is expected to further enhance the precision and efficacy of personalized treatments56.

Artificial intelligence (AI) has become an indispensable tool in genomic data analysis, significantly enhancing the ability to interpret complex genetic information. AI algorithms, particularly those involving machine learning and neural networks, can rapidly identify genetic mutations and predict disease progression by analyzing vast amounts of genomic data12. For instance, AI-driven models have been developed to isolate cell-type-specific transcriptomic modules that underlie diseases like Alzheimer's, providing insights into subcellular disease progression trajectories1. Moreover, AI-powered genomic analysis has revolutionized the detection of genetic mutations, enabling the rapid identification of disease-causing variants and expediting the diagnostic process for rare genetic diseases25. Explainable AI (XAI) approaches are also emerging, offering transparency in model predictions, which is crucial for clinical applications in genomics3. Additionally, AI applications in pathogen genomics have advanced, aiding in the identification of novel pathogens and the prediction of antimicrobial resistance, thus supporting public health initiatives4. These advancements underscore the transformative potential of AI in genomics, paving the way for more precise and personalized medical interventions.