AUTONOMOUS ONCOLOGY SYSTEMS: FOUNDATION MODELS FOR REAL-TIME CLINICAL INTELLIGENCE AND PERSONALIZED CANCER CARE

Abstract

Artificial intelligence (AI) is rapidly transforming oncology from a reactive clinical discipline into an autonomous, continuously learning healthcare ecosystem capable of supporting real-time clinical intelligence and personalized cancer care. Conventional clinical decision-making often relies on episodic assessments, fragmented biomedical information, and static predictive models that inadequately reflect the dynamic biological evolution of cancer. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled the development of autonomous oncology systems capable of integrating radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, liquid biopsy, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes into unified patient-specific computational frameworks. These intelligent systems continuously learn from evolving biomedical evidence while supporting cancer diagnosis, biomarker discovery, 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, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen autonomous oncology by enabling collaborative, privacy-preserving, and continuously adaptive computational medicine. 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 oncology systems, emphasizing foundation AI models for real-time clinical intelligence and personalized cancer care as a transformative paradigm in precision oncology.