INTELLIGENT CANCER FOUNDATION MODELS: INTEGRATING MOLECULAR, IMAGING, AND CLINICAL INTELLIGENCE FOR PRECISION MEDICINE

Abstract

Artificial intelligence (AI) is transforming precision oncology through the emergence of intelligent foundation models capable of integrating heterogeneous biomedical information into unified computational frameworks supporting personalized cancer care. Conventional machine learning algorithms have demonstrated substantial success in isolated oncology applications; however, their dependence on task-specific training, fragmented analysis of multimodal datasets, and limited generalizability restrict their clinical scalability. Intelligent cancer foundation models overcome these limitations through large-scale self-supervised learning, multimodal transformer architectures, graph neural networks, and generative AI that learn generalized biomedical representations from radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational ecosystems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further enhance foundation model ecosystems by enabling collaborative, privacy-preserving, and continuously adaptive computational oncology. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of intelligent cancer foundation models, emphasizing integration of molecular, imaging, and clinical intelligence as a transformative paradigm for precision medicine.