AGENTIC ARTIFICIAL INTELLIGENCE IN PRECISION ONCOLOGY: AUTONOMOUS MULTIMODAL SYSTEMS FOR PERSONALIZED CANCER CARE

Abstract

Agentic artificial intelligence (AI) represents the next frontier in precision oncology by enabling autonomous computational systems capable of perceiving, reasoning, planning, executing, and continuously learning across the entire cancer care continuum. Unlike conventional AI systems that perform isolated predictive tasks, agentic AI integrates foundation models, multimodal learning, digital twins, reinforcement learning, and clinical reasoning into intelligent ecosystems capable of autonomously coordinating diagnostic workflows, therapeutic planning, longitudinal disease monitoring, and personalized clinical decision-making. Recent advances in multimodal transformer architectures, graph neural networks, self-supervised 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, and real-world clinical outcomes into unified computational frameworks. These intelligent systems support early cancer detection, molecular characterization, biomarker discovery, prognostic prediction, treatment optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and intelligent clinical decision support. Emerging technologies including multimodal large language models, federated learning, retrieval-augmented generation, explainable artificial intelligence, and autonomous planning algorithms further strengthen agentic 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, interoperability, cybersecurity, clinical validation, human oversight, and equitable implementation. This review provides a comprehensive overview of agentic artificial intelligence in precision oncology, emphasizing autonomous multimodal systems for personalized cancer care and clinical translation.