Reconstructing General Medicine: Integrative Frameworks for Precision and Population Health

The convergence of precision medicine, population health, digital technologies, multi-omics, artificial intelligence (AI), and data-driven healthcare increasingly shapes general medicine.This review examines integrative frameworks that connect individualized clinical care with population-level prevention, surveillance, and health-system planning.The reviewed evidence highlights the expanding role of genomics, phenomics, multi-omics, predictive analytics, digital health, and AI in disease characterization, risk stratification, clinical decision-making, and personalized treatment.Population-health approaches complement these advances by incorporating environmental exposures, behavioral factors, social determinants, occupational health, community resilience, and public health preparedness.Big data and computational modeling further support the integration of heterogeneous clinical, molecular, environmental, and population datasets for predictive and preventive applications.The review also identifies challenges related to data interoperability, algorithmic bias, privacy, unequal digital infrastructure, limited external validation, and inconsistent implementation across healthcare settings.Emerging strategies such as explainable AI, digital twins, standardized data architectures, and multidisciplinary implementation models may improve clinical translation.This review reports that integrating precision-oriented approaches with population-health frameworks can support a more preventive, personalized, equitable, and resilient model of general medicine capable of addressing both individual biological variability and broader determinants of health.