Multi-Omics Biomarkers From Variant to Clinic: A Systematic Review and Meta-Analysis of Evidence, AI/ML, Governance, Equity, and Real-World Implementation Across Global Health Systems.

Background/Purpose: Multi-omics integration linking genomic, transcriptomic, epigenomic, proteomic, metabolomic, single-cell, and spatial data has transformed the interpretation of human genetic variation by capturing molecular processes that extend beyond DNA sequence alone. Although these approaches substantially improve biomarker discovery and disease stratification, translation into clinical practice remains uneven due to methodological heterogeneity, limited validation, regulatory uncertainty, and structural inequities in data generation. This systematic review and meta-analysis aimed to evaluate scientific performance, clinical readiness, governance frameworks, and socio-technical constraints influencing multi-omics biomarker development, and to generate a roadmap for equitable global implementation. Methods: Following PRISMA 2020 guidelines, we systematically searched PubMed, EMBASE, Web of Science, Scopus, medRxiv, and bioRxiv for studies published between January 2010 and December 2025. Eligible articles integrated ≥ 2 omics modalities, applied AI/ML to biomarker development or variant interpretation, assessed clinical utility or real-world implementation, or examined governance, ethics, consent, equity, or policy issues. Data extraction captured assay type, integration strategy, model performance, validation rigor, and regulatory or socio-technical insights. Random-effects meta-analyses estimated pooled improvements in AUC, sensitivity, specificity, and hazard ratio precision, and heterogeneity was assessed using I² statistics. Results: From 9846 records, 528 studies met the inclusion criteria. Multi-omics integration improved predictive performance, yielding pooled gains of +0.16 in AUC (95% CI: 0.11-0.19), +13% in sensitivity, and +9% in specificity. Models combining ≥ 3 omics layers showed the largest improvements (+0.19 AUC). Single-cell and spatial assays enhanced risk stratification by 18% but demonstrated reproducibility limitations. AI/ML approaches added +0.12 AUC over traditional models, yet 67% exhibited ancestry bias, and only 22% implemented explainability tools. Only 19% of biomarkers underwent real-world evaluation due to limited validation, reimbursement gaps, interoperability challenges, and unclear data-rights governance. Conclusion: Multi-omics biomarkers offer substantial analytical advantages, but their translation requires standardized validation frameworks, accountable AI governance, interoperable infrastructure, and globally inclusive data sets to ensure equitable, trustworthy implementation.