AI-driven multi-omics integration in cancer research and precision oncology

Integrating multi-omics data with Artificial Intelligence offers new opportunities for understanding cancer biology and discovering actionable biomarkers. In this selective review aimed at clinicians and translational researchers, we survey methodological innovations from classical machine learning to deep learning. We synthesize recent innovations, identify persistent technical challenges, and evaluate the landscape of available multi-omics datasets. Ultimately, we propose a strategic roadmap for translating AI-driven integrative models into real-world precision oncology.