Computational Precision Oncology: Integrating Foundation Ai Models With Multi-Omics And Clinical Intelligence

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The rapid expansion of biomedical data generated from radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, epigenomics, laboratory investigations, wearable technologies, and electronic health records has created unprecedented opportunities for computational oncology while simultaneously presenting major analytical challenges. Computational precision oncology has emerged as a transformative discipline that combines artificial intelligence (AI), foundation models, multimodal learning, multi-omics integration, systems biology, and clinical intelligence to enable individualized cancer diagnosis, prognostic prediction, therapeutic optimization, and longitudinal disease monitoring. Foundation AI models pretrained through self-supervised learning acquire generalized biomedical representations that can be adapted across diverse oncology applications including radiogenomics, computational pathology, biomarker discovery, immunotherapy prediction, digital twins, drug discovery, and clinical decision support. Advances in transformer architectures, graph neural networks, multimodal large language models, retrieval-augmented generation, agentic AI, and generative artificial intelligence have substantially accelerated the development of intelligent computational oncology ecosystems capable of integrating heterogeneous biomedical information into unified patient-centered representations. Despite remarkable progress, important challenges remain regarding multimodal data harmonization, computational scalability, explainability, interoperability, cybersecurity, regulatory validation, and equitable implementation. This review provides a comprehensive overview of computational precision oncology, emphasizing the integration of foundation AI models, multi-omics, and clinical intelligence for next-generation personalized cancer care.[1]