Artificial Intelligence (AI) and machine learning (ML) are revolutionizing precision oncology by integrating and interpreting multi-omics data to uncover patient-specific biomarkers, predict therapeutic response, and guide personalized treatment strategies. AI-driven multi-omics integration faces challenges such as high dimensionality, data heterogeneity, and interpretability, which are critical for effective translation to precision oncology. This article provides a comprehensive overview of the current landscape and future trajectory of AI and ML in integrated multi-omics analysis for cancer research. The discussion explores the application of AI/ML across key omics modalities, including bulk RNA sequencing, single-cell RNA sequencing, spatial transcriptomics, and genomics, highlighting both individual and combined analytical approaches. The report elucidates advanced integration methodologies, including deconvolution, label transfer, and spatial mapping, alongside the inherent challenges of data heterogeneity, high dimensionality, and interpretability. Here, we further investigate real-world barriers to clinical translation, regulatory and ethical challenges, and demonstrate significant clinical impact through compelling case studies. This review explores current research gaps and outlines future directions, highlighting the contribution of AI-driven integrated omics in enhancing precision oncology.
Yadav et al. (2026) studied this question.