Nada Nabil Saad

Precision medicine, which relies on genomic, multi-omic, phenotypic, and environmental data, has the potential to transform healthcare from population-focused heuristics to individualized prevention, diagnosis, and treatment. Moreover, recent advances in sequencing, molecular profiles, wearable sensors, and machine learning have created opportunities for rapid translational innovation: rapid genomic diagnosis in neonatal and paediatric rare diseases, targeted oncology, pharmacogenomic-based prescribing strategies, and individual sport performance. Nevertheless, the vast majority of innovations remain in centers of specialism or pilot programs, rather than routinely or equitably integrated into clinical or athletic practice. This narrative review synthesizes translational evidence across the life course—in pregnancy, paediatrics, adult medicine, geriatrics, and sportomics—to find reproducible clinical and performance examples which enable precision-based alternative approaches to management, outcome, or preparation; and to reshape those examples into pragmatic, scalable priorities which minimize inequity, and maximize benefit. We undertook a structured narrative synthesis of peer-reviewed literature, trials, clinician translation programs, implementation studies, and sportomics reports, prioritizing examples that demonstrate utility, reproducibility, and impact. Important findings suggest that multi-omics and rapid sequencing improve diagnostic yield and time to diagnosis. Molecular profiling and circulating tumor DNA help realize adaptive treatment selection. Integrated genomics, metabolomics, wearable physiology, and AI analytics facilitate individualized training, injury-risk stratification, and recovery optimization. But systematic value is limited by insufficient representative validation, dataset bias, poor interoperability, regulatory uncertainty, workforce preparedness, and inequities of access. Converting a promise into population- and performance-level value requires coordinated action across four fronts: representative validation; interoperable, privacy-preserving infrastructures; clinician- and coach-centered implementation; and templates for scalable, cost-sensitive deployment.