Converging Wearable Biosensors, Multi-Omics, and Artificial Intelligence: An Integrative Framework for Predictive and Preventive Precision Medicine

Abstract

Precision medicine aspires to tailor prevention, diagnosis, and treatment to the individual rather than to a statistical average, yet that ambition has long outrun the data and the tools needed to deliver it. Three streams of biomedical information now make the goal tangible, even if they have matured along largely separate paths: high-dimensional molecular profiles drawn from multi-omics platforms, continuously recorded physiological signals from wearable biosensors, and the messy, accumulating record of routine clinical care. Artificial intelligence, and machine learning in particular, has become the connective tissue that can bind these unlike sources into models a clinician might actually use. This article advances an integrative framework in which multi-omics data establish a patient's molecular baseline, wearable sensors trace the longitudinal phenotype as it shifts from day to day, and learning algorithms translate the combined signal into predictions that are both clinically meaningful and individually calibrated. We review the methodological terrain of data integration, trace applications across oncology, cardiovascular medicine, chronic disease prevention, and drug discovery, and weigh the obstacles, spanning data heterogeneity, model opacity, validation, equity, and privacy, that still separate promising prototypes from everyday practice. Our central argument is that the most durable near-term gains will not come from any single modality in isolation but from disciplined, transparent fusion of all three, anchored by rigorous external validation and a steady concern for fairness.