Artificial Intelligence for Adaptive Precision Oncology: Continuous Learning Models Across the Cancer Care Continuum

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 dynamic nature of cancer biology, characterized by continuous genomic evolution, intratumoral heterogeneity, treatment-induced resistance, and complex interactions within the tumor microenvironment, requires adaptive clinical strategies that evolve throughout the patient's disease course. Recent advances in artificial intelligence (AI) have introduced continuous learning models capable of integrating radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical data into continuously evolving computational systems. Unlike conventional predictive models trained on static datasets, adaptive AI systems continuously learn from new patient information, enabling real-time refinement of diagnosis, prognostic prediction, therapeutic optimization, toxicity assessment, disease monitoring, and survivorship planning. Advances in machine learning, deep learning, reinforcement learning, transformer architectures, multimodal foundation models, graph neural networks, federated learning, digital twins, and agentic AI have accelerated development of intelligent oncology ecosystems capable of supporting personalized medicine across the entire cancer care continuum. Despite remarkable progress, significant challenges remain regarding continual learning, catastrophic forgetting, interoperability, explainability, cybersecurity, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of adaptive artificial intelligence in precision oncology, highlighting computational foundations, clinical applications, implementation challenges, and future perspectives for continuously learning cancer care systems.[1]