ADAPTIVE FOUNDATION MODELS IN ONCOLOGY: CONTINUOUS LEARNING FOR PERSONALIZED CANCER MANAGEMENT

Abstract

Artificial intelligence (AI) is transforming oncology from a static decision-support technology into a continuously adaptive computational ecosystem capable of learning from evolving biomedical knowledge and patient-specific clinical trajectories. Conventional machine learning algorithms remain fixed after deployment and often struggle to accommodate the dynamic nature of tumor evolution, therapeutic resistance, changing clinical guidelines, and rapidly expanding biomedical evidence. Adaptive foundation models overcome these limitations through large-scale self-supervised learning, continual learning, multimodal transformer architectures, graph neural networks, reinforcement learning, and generative AI, enabling continuous refinement of predictive performance throughout the cancer care continuum. These intelligent computational systems integrate radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, patient-reported outcomes, and longitudinal clinical data into unified patient-specific representations. Adaptive foundation models support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, intelligent clinical decision support, and personalized survivorship management while continuously incorporating new biomedical evidence. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen adaptive computational oncology by enabling collaborative, privacy-preserving, and continuously evolving precision medicine. Despite remarkable technological progress, 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 adaptive foundation models in oncology, emphasizing continuous learning as a transformative paradigm for personalized cancer management.