Next-Generation Cancer Intelligence Platforms: Converging Foundation Models, Digital Twins, and Precision Medicine

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine. The rapid expansion of biomedical data generated from radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, and electronic health records has created unprecedented opportunities for computational oncology while simultaneously presenting substantial challenges for clinical interpretation. Nextgeneration cancer intelligence platforms have emerged as an integrated computational paradigm that combines foundation models, digital twin technology, multimodal artificial intelligence (AI), systems biology, and precision medicine into continuously learning clinical ecosystems. Unlike conventional decision-support systems that analyze isolated datasets, these intelligent platforms integrate multimodal biomedical information to generate adaptive patient-specific models capable of supporting diagnosis, molecular characterization, therapeutic optimization, toxicity prediction, digital simulation, clinical trial matching, and longitudinal disease monitoring. Advances in transformer architectures, multimodal foundation models, graph neural networks, large language models, agentic AI, reinforcement learning, generative AI, federated learning, and cloudnative computing have accelerated development of intelligent oncology ecosystems capable of real-time clinical reasoning and personalized therapeutic decision-making. Despite substantial progress, challenges remain regarding interoperability, computational scalability, explainability, cybersecurity, ethical governance, regulatory validation, and equitable clinical implementation. This review provides a comprehensive overview of next-generation cancer intelligence platforms, highlighting computational principles, clinical applications, implementation challenges, and future perspectives for the convergence of foundation models, digital twins, and precision medicine.