AGENTIC FOUNDATION MODELS FOR PRECISION ONCOLOGY: AUTONOMOUS CLINICAL INTELLIGENCE ACROSS THE CANCER CARE CONTINUUM

Abstract

Agentic foundation models represent the next evolutionary stage of artificial intelligence (AI) in precision oncology by integrating autonomous reasoning, multimodal clinical intelligence, adaptive decision support, and continuous learning across the cancer care continuum. Unlike conventional AI systems that perform isolated predictive tasks, agentic foundation models coordinate complex clinical workflows through autonomous planning, evidence synthesis, multimodal reasoning, and intelligent collaboration with healthcare professionals under human supervision. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, retrieval-augmented generation, 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, and longitudinal clinical outcomes into unified computational representations of cancer biology. These intelligent systems support precision diagnosis, biomarker discovery, prognostic prediction, therapeutic optimization, adaptive clinical trials, digital twin simulation, survivorship management, and evidence-based multidisciplinary decision-making. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, cloud-native healthcare platforms, digital health ecosystems, robotics, and autonomous clinical orchestration further strengthen agentic oncology systems by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, regulatory, and implementation challenges remain regarding multimodal data harmonization, computational scalability, interoperability, cybersecurity, clinical validation, governance, and equitable implementation. This review provides a comprehensive overview of agentic foundation models for precision oncology, emphasizing autonomous clinical intelligence across the entire cancer care continuum