Cognitive Radiology: Artificial Intelligence in Oncology Imaging and Personalized Cancer Care

Abstract

Background: Medical imaging plays a central role throughout the cancer care continuum, supporting early detection, diagnosis, staging, treatment planning, therapeutic monitoring, and long-term surveillance. However, the increasing complexity and volume of imaging data generated by computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound, mammography, and hybrid imaging technologies have exceeded the interpretive capacity of conventional radiological workflows. Recent advances in artificial intelligence (AI), computational imaging, radiomics, radiogenomics, multimodal learning, and cognitive radiology have transformed oncology imaging by enabling automated image interpretation, quantitative biomarker extraction, molecular prediction, and personalized clinical decision support. Cognitive radiology refers to the integration of AI with advanced medical imaging to emulate aspects of expert radiological reasoning through continuous analysis of imaging, pathology, genomics, laboratory investigations, electronic health records, and longitudinal clinical information. Machine learning, deep learning, transformer architectures, foundation models, graph neural networks, reinforcement learning, and explainable artificial intelligence have significantly enhanced tumor detection, lesion characterization, treatment response assessment, adaptive imaging, and personalized cancer management. Despite remarkable progress, challenges remain regarding data standardization, interoperability, computational scalability, algorithmic transparency, regulatory validation, cybersecurity, and equitable clinical implementation. This review provides a comprehensive overview of cognitive radiology and artificial intelligence in oncology imaging, highlighting computational foundations, current clinical applications, emerging innovations, and future perspectives for personalized cancer care.