ABSTRACT
Precision oncology is entering a new era driven by foundation artificial intelligence (AI) models capable of integrating heterogeneous biomedical information into unified computational ecosystems for individualized cancer care. Traditional machine learning algorithms have demonstrated considerable success in isolated oncology applications; however, their dependence on task-specific training and single-modality datasets limits scalability, generalizability, and clinical translation. Foundation AI models overcome these limitations through large-scale self-supervised learning, enabling generalized biomedical representation learning across radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, electronic health records, wearable technologies, and longitudinal clinical information. These models constitute the computational core of intelligent foundation model ecosystems that continuously integrate multimodal data for diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, disease monitoring, digital twin simulation, and clinical decision support. Recent advances in transformer architectures, graph neural networks, multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, agentic AI, and generative models have further expanded the capabilities of computational oncology by enabling adaptive, collaborative, and continuously learning clinical ecosystems. Despite remarkable technological progress, significant challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, regulatory validation, cybersecurity, and ethical governance. This review provides a comprehensive overview of foundation model ecosystems for precision oncology, highlighting computational principles, multimodal intelligence, clinical applications, implementation challenges, and future directions for integrating artificial intelligence throughout cancer care.[1]
Keywords: Foundation models, Precision oncology, Artificial intelligence, Multimodal learning, Computational oncology, Digital pathology, Radiomics, Multi-omics, Clinical decision support, Personalized medicine
