PREDICTIVE ONCOLOGY THROUGH FOUNDATION AI: INTEGRATING LONGITUDINAL CLINICAL DATA, MULTI-OMICS, AND DIGITAL TWINS

Abstract

Predictive oncology is undergoing a transformative evolution through the integration of foundation artificial intelligence (AI) models, longitudinal clinical data, multi-omics technologies, and digital twin systems into comprehensive computational ecosystems capable of anticipating disease progression, therapeutic response, recurrence, and survival. Conventional oncology frequently relies on static clinical assessments and isolated molecular analyses, limiting accurate prediction of evolving tumor biology and individualized treatment outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled seamless integration of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, biomedical literature, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, adaptive disease monitoring, digital twin simulation, intelligent clinical decision support, and personalized cancer care. Emerging technologies including multimodal large language models, agentic AI, federated learning, retrieval-augmented generation, explainable artificial intelligence, cancer knowledge graphs, cloud-native healthcare platforms, and digital health ecosystems further strengthen predictive oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, regulatory acceptance, and equitable implementation. This review provides a comprehensive overview of predictive oncology through foundation AI, emphasizing integration of longitudinal clinical data, multi-omics, and digital twins for next-generation precision oncology.