AUTONOMOUS PRECISION ONCOLOGY: THE CONVERGENCE OF FOUNDATION AI MODELS, DIGITAL TWINS, AND COMPUTATIONAL CANCER INTELLIGENCE

Abstract

Autonomous precision oncology represents the next evolutionary stage of computational cancer medicine, where foundation artificial intelligence (AI) models, digital twins, multimodal learning, and computational cancer intelligence converge to create continuously adaptive clinical ecosystems capable of supporting personalized diagnosis, prognostic prediction, therapeutic optimization, and longitudinal disease management. Conventional oncology frequently relies on fragmented diagnostic workflows, episodic clinical assessments, and isolated interpretation of imaging, pathology, molecular diagnostics, and clinical information, limiting comprehensive understanding of the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, reinforcement learning, and generative artificial intelligence have enabled unified analysis of radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support early cancer detection, biomarker discovery, molecular characterization, prognostic prediction, therapeutic 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 agentic AI further strengthen autonomous oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical reasoning. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interpretability, interoperability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of autonomous precision oncology, emphasizing the convergence of foundation AI models, digital twins, and computational cancer intelligence as a transformative paradigm for next-generation personalized cancer medicine.