• Integrative multi-omics and machine learning significantly enhance disease subtype classification and biomarker discovery.
• Deep learning models, particularly graph neural networks, outperform traditional methods in predicting variant pathogenicity.
• Federated learning enables privacy-preserving data sharing across institutions, improving model generalizability.
• Explainable AI techniques are crucial for clinical adoption, providing interpretable predictions that align with medical knowledge.