Artificial Intelligence for Real-Time Oncology: Continuous Learning Systems for Personalized Cancer Care

Abstract

Artificial intelligence (AI) is transforming oncology from a reactive discipline into a continuously adaptive and learning healthcare ecosystem. Traditional cancer management relies on episodic clinical assessments, static treatment protocols, and infrequent updates of patient information, often limiting the ability to respond promptly to rapidly evolving tumor biology. Recent advances in continuous learning systems, multimodal foundation AI models, digital pathology, radiomics, multi-omics, wearable technologies, liquid biopsy, electronic health records, and real-world clinical data have created unprecedented opportunities for real-time oncology. These intelligent systems continuously integrate heterogeneous biomedical information to generate dynamic patient-specific computational representations that evolve alongside disease progression, therapeutic response, and physiological changes. Unlike conventional machine learning algorithms that remain fixed after deployment, continuous learning systems iteratively update predictive models using newly acquired clinical evidence while supporting adaptive diagnosis, treatment optimization, toxicity prediction, recurrence monitoring, and personalized survivorship care. Advances in transformer architectures, graph neural networks, reinforcement learning, federated learning, agentic AI, retrieval-augmented generation, digital twins, and large language models are further expanding the capabilities of real-time computational oncology. Despite remarkable progress, important challenges remain regarding data harmonization, computational scalability, explainability, cybersecurity, regulatory validation, algorithmic stability, and ethical governance. This review provides a comprehensive overview of AI-driven real-time oncology, emphasizing continuous learning systems, computational architectures, clinical applications, implementation challenges, and future perspectives for personalized cancer care.[1]