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PM uses diagnostic assessments to tailor treatment through individualized medical interventions. PM may improve therapeutic precision beyond traditional trial-and-error approaches, reduce adverse consequences, and improve outcomes by integrating genomic and transcriptomic data. PM considers genetic and molecular landscapes, immunologic factors, epigenetic influences, and environmental exposures to assess treatment response. However, challenges remain related to diagnostic access, the slow pace of biomarker identification, technological limitations, sustained patient engagement, data management, and computational requirements. Nevertheless, continued efforts to improve understanding of disease pathophysiology, gene expression, and immune regulation\u2014together with the application of novel technologies and machine learning\u2014may advance PM-based therapies. Additional opportunities include drug\u2013target modeling and exploratory single-cell\u2013based approaches to clarify patient-specific therapeutic mechanisms. 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