Berger Noah¹, Franka Lea², Lindner Samuel³, Ludwig Hanna⁴, Arnold Emil⁵, Simon Leni⁶, Ernst Moritz⁷, Thomas Lilly⁸, Kraus Julian⁹
ABSTRACT:
The convergence of artificial intelligence (AI) and high-throughput omics technologies is revolutionizing cardiovascular biomarker discovery. This review examines: (1) AI-driven platforms for large-scale proteomic (Olink, SomaScan) and metabolomic (LC-MS, NMR) profiling in population cohorts; (2) deep learning architectures (CNNs, GNNs) for pattern recognition in multi-omics datasets; and (3) clinical applications in early disease detection (subclinical atherosclerosis), phenotyping (HFpEF subtypes), and treatment personalization (anticoagulant selection). We evaluate performance metrics of AI-omics models from recent studies (UK Biobank, Framingham Heart Study), highlighting integrated biomarker panels with >90% AUC for incident CVD prediction. Technical challenges in data harmonization, algorithmic bias, and regulatory approval are critically discussed, along with emerging paradigms like federated learning for privacy-preserving biomarker development and quantum computing for real-time omics analysis.