Towards Autonomous Precision Oncology: AI Agents, Digital Twins, and Multimodal Clinical Intelligence for Personalized Cancer Care

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The extraordinary biological complexity of malignant diseases, combined with rapidly expanding volumes of radiological imaging, digital pathology, genomic sequencing, multi-omics, laboratory investigations, wearable technologies, and longitudinal clinical data, has created an urgent need for intelligent computational systems capable of supporting personalized oncology. Recent advances in artificial intelligence (AI) have introduced the concept of autonomous precision oncology, where AI agents, digital twins, multimodal foundation models, and real-time clinical intelligence operate together within integrated healthcare ecosystems. Unlike conventional AI systems designed for isolated prediction tasks, autonomous oncology platforms continuously perceive, reason, plan, execute, monitor, and learn from dynamic clinical environments while maintaining physician oversight. AI agents coordinate diagnostic workflows, digital twins simulate disease evolution and therapeutic response, and multimodal clinical intelligence integrates imaging, pathology, multi-omics, electronic health records, and real-world clinical outcomes into comprehensive patient-specific computational representations. Advances in transformer architectures, graph neural networks, reinforcement learning, retrievalaugmented generation, agentic AI, federated learning, and generative AI have significantly accelerated development of these intelligent oncology ecosystems. Despite remarkable progress, important challenges remain regarding explainability, interoperability, computational scalability, cybersecurity, regulatory validation, ethical governance, clinician acceptance, and equitable implementation. This review presents a comprehensive overview of autonomous precision oncology, highlighting computational principles, clinical applications, implementation challenges, and future perspectives for personalized cancer care driven by intelligent computational ecosystems.[1]