Digital health constitutes a critical enabler of the ongoing transformation from a predominantly diagnostic and therapeutic medical paradigm toward one centered on prediction, prevention, personalization, and patient participation, in alignment with the principles of P4 medicine. Advances in genomics, digital biomarkers and the systematic integration of patient-specific data already permit the early identification of risk factors at an individual level. When combined across genomic, environmental, behavioral, and familial dimensions, such data provide the foundation for predictive analytics that can inform preventive strategies, thereby extending their application from specialized settings into routine primary care. The incorporation of Artificial Intelligence, Machine Learning, and Large Language Models into these data-driven frameworks has the potential to operationalize precision public health, characterized by individualized care pathways, automated clinical workflows, and adaptive decision support systems. In parallel, digital health innovation supports the democratization of healthcare access by reducing systemic barriers, fostering scalability, and simultaneously safeguarding quality standards. Importantly, personalization within this context is not limited to genomic or molecular determinants but extends to the dynamic interplay of environmental exposures, social determinants of health, and individual preferences, resulting in evidence-based and person-centered interventions. Together, these developments position digital health as a cornerstone for sustainable, equitable, and high-quality healthcare systems of the future.