NEXT-GENERATION INTELLIGENT ONCOLOGY: CONVERGING AGENTIC AI, FOUNDATION MODELS, AND PRECISION MEDICINE FOR PERSONALIZED CANCER CARE

Abstract

Next-generation intelligent oncology is emerging as a transformative paradigm in precision medicine through the convergence of agentic artificial intelligence (AI), foundation models, multimodal biomedical intelligence, and adaptive clinical decision support. Conventional precision oncology has significantly improved diagnosis and targeted therapy through molecular characterization; however, fragmented biomedical data, limited interoperability, static predictive models, and increasing clinical complexity continue to challenge personalized cancer management. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, agentic AI, 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. Agentic AI extends these capabilities through autonomous planning, workflow orchestration, adaptive reasoning, evidence synthesis, and intelligent collaboration with multidisciplinary oncology teams under continuous human supervision. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, digital twin simulation, adaptive clinical trials, survivorship management, and evidence-based personalized cancer care. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, cancer knowledge graphs, cloud-native healthcare platforms, robotics, and digital health ecosystems further strengthen intelligent oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of next-generation intelligent oncology, emphasizing convergence of agentic AI, foundation models, and precision medicine for personalized cancer care.