Artificial Intelligence Across the Oncology Continuum: From Prevention to Survivorship

Abstract

Artificial intelligence (AI) is transforming cancer care by supporting decision-making across every stage of the oncology continuum, from primary prevention and early detection to diagnosis, treatment selection, survivorship, and palliative care. Rapid advances in machine learning, deep learning, natural language processing, multimodal foundation models, and generative AI have enabled the analysis of complex biomedical data derived from medical imaging, digital pathology, genomics, electronic health records, wearable sensors, and patient-reported outcomes. These computational technologies facilitate individualized risk prediction, improve screening efficiency, optimize therapeutic planning, and enable continuous patient monitoring throughout the cancer journey. AI-driven systems are increasingly integrated into clinical workflows for radiology, pathology, molecular oncology, precision medicine, radiation therapy, surgical planning, and supportive care, thereby enhancing both clinical efficiency and patient outcomes.[1] Nevertheless, significant challenges remain regarding data quality, algorithmic bias, explainability, interoperability, cybersecurity, ethical governance, regulatory approval, and equitable implementation across diverse healthcare settings.[2] This review discusses the evolving role of artificial intelligence throughout the oncology continuum, emphasizing its applications in cancer prevention, early detection, diagnosis, precision medicine, and treatment planning while highlighting future opportunities for responsible clinical adoption.