Abstract
Digital technology and artificial intelligence have revolutionized predictive models based on clinical data, creating opportunities for proactive health management. This review systematically evaluates the role and effectiveness of biomarker-driven predictive models across disease detection, personalized intervention, and healthcare resource optimization. Critical challenges hindering their implementation include data heterogeneity, inconsistent standardization protocols, limited generalizability across populations, high implementation costs, and substantial barriers in clinical translation. To address these challenges, we propose an integrated framework prioritizing three pillars: multi-modal data fusion, standardized governance protocols, and interpretability enhancement, systematically addressing implementation barriers from data heterogeneity to clinical adoption. This systematic approach enhances early disease screening accuracy while supporting risk stratification and precision diagnosis, particularly for chronic conditions and oncology applications. By effectively connecting biomarker discovery with practical clinical utilization, our proposed framework offers actionable methodologies that address existing limitations while guiding multidisciplinary research teams. Moving forward, expanding these predictive models to rare diseases, incorporating dynamic health indicators, strengthening integrative multi-omics approaches, conducting longitudinal cohort studies, and leveraging edge computing solutions for low-resource settings emerge as critical areas requiring innovation and exploration.