AI-Augmented Clinical Oncology: Transforming Personalized Cancer Care Through Digital Intelligence

Abstract

Artificial intelligence (AI) is fundamentally transforming clinical oncology by augmenting medical expertise with advanced computational intelligence capable of integrating diverse biomedical information across the entire cancer care continuum. The convergence of machine learning, deep learning, foundation AI models, multimodal transformers, graph neural networks, large language models, and generative artificial intelligence has created powerful digital ecosystems that support cancer screening, diagnosis, prognostic prediction, biomarker discovery, therapeutic optimization, clinical decision support, and survivorship management. Unlike conventional healthcare systems that often analyze isolated clinical variables, AI-augmented oncology integrates radiological imaging, digital pathology, genomics, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, electronic health records, patient-reported outcomes, and real-world clinical data into comprehensive patient-specific computational representations. These intelligent systems enable adaptive precision medicine by continuously learning from evolving biomedical evidence and longitudinal patient trajectories. Emerging technologies such as digital twins, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further expand the capabilities of computational oncology while supporting personalized therapeutic strategies and multidisciplinary clinical decision-making. Despite remarkable technological progress, important challenges remain regarding multimodal data harmonization, algorithmic transparency, interoperability, cybersecurity, regulatory validation, ethical governance, and equitable implementation. This review provides a comprehensive overview of AI-augmented clinical oncology, emphasizing technological foundations, clinical applications, implementation challenges, and future perspectives for transforming personalized cancer care through digital intelligence.[1]