THE FUTURE OF PRECISION ONCOLOGY: CONVERGING FOUNDATION MODELS, AGENTIC AI, DIGITAL TWINS, AND MULTIMODAL CLINICAL INTELLIGENCE

Abstract

Precision oncology is entering a transformative era driven by the convergence of foundation artificial intelligence (AI) models, agentic AI, digital twins, multimodal clinical intelligence, and continuously learning healthcare ecosystems. Traditional precision oncology has significantly improved diagnosis and treatment by incorporating molecular profiling and targeted therapies; however, fragmented biomedical data, limited interoperability, delayed clinical decision-making, and insufficient integration of heterogeneous patient information continue to constrain personalized cancer care. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled comprehensive 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, digital biomarkers, and longitudinal clinical outcomes into unified computational representations of cancer biology. Agentic AI systems extend these capabilities by autonomously coordinating diagnostic workflows, synthesizing biomedical evidence, optimizing therapeutic recommendations, monitoring disease progression, and supporting multidisciplinary clinical decision-making under human supervision. Simultaneously, digital twin technology enables continuously evolving virtual representations of individual patients capable of simulating disease progression, therapeutic response, toxicity, recurrence, and personalized treatment strategies. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, cloud-native healthcare platforms, quantum computing, robotics, and digital health ecosystems further strengthen computational oncology by enabling collaborative, transparent, privacy-preserving, and adaptive clinical intelligence. Despite remarkable technological progress, 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 the future of precision oncology, emphasizing the convergence of foundation models, agentic AI, digital twins, and multimodal clinical intelligence as transformative technologies for next-generation cancer medicine.