FOUNDATION AI AGENTS IN ONCOLOGY: AUTONOMOUS CLINICAL INTELLIGENCE FOR PERSONALIZED CANCER MANAGEMENT

Abstract

Foundation artificial intelligence (AI) agents are emerging as a transformative paradigm in precision oncology by enabling autonomous clinical intelligence capable of perceiving, reasoning, planning, executing, and continuously learning across the entire cancer care continuum. Unlike conventional AI systems that perform isolated prediction tasks, foundation AI agents integrate multimodal foundation models, large language models, digital twins, reinforcement learning, graph neural networks, and autonomous reasoning into intelligent computational ecosystems capable of coordinating diagnostic workflows, therapeutic planning, longitudinal disease monitoring, biomarker discovery, and personalized clinical decision-making. Recent advances in multimodal transformer architectures, self-supervised learning, generative artificial intelligence, retrieval-augmented generation, and agentic AI 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 frameworks. These intelligent systems support early cancer detection, molecular characterization, prognostic prediction, immunotherapy selection, digital twin simulation, adaptive disease monitoring, therapeutic optimization, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, explainable artificial intelligence, cloud-native healthcare platforms, and Internet of Medical Things (IoMT) technologies further strengthen autonomous oncology 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, interpretability, cybersecurity, clinical validation, human oversight, and equitable implementation. This review provides a comprehensive overview of foundation AI agents in oncology, emphasizing autonomous clinical intelligence for personalized cancer management.