Rise of the Augmented Radiologist: A Vision for 2035

Artificial intelligence (AI) is rapidly transforming diagnostic imaging, with developments in deep learning, automated triage systems, and advanced decision-support tools progressively reshaping radiological practice. By 2035, the integration of multimodal analytics, computational pathology, and real-time clinical prediction models is expected to converge toward a new paradigm: the augmented radiologist. This model envisions enhanced diagnostic accuracy, improved workflow efficiency, and expanded clinical roles, driven by AI-enabled automation and human–machine collaboration. Through a narrative review of current literature, this paper examines the technical foundations underlying radiology augmentation, evaluates the projected clinical and workflow implications, and identifies challenges related to dataset bias, validation standards, ethical governance, and medico-legal considerations. Evidence suggests that augmentation rather than replacement of radiologists will dominate the future of imaging, as human expertise remains essential for contextual interpretation, ethical oversight, and complex clinical decision-making. This manuscript provides a forward-looking analysis to guide institutions, policymakers, and educators in preparing for the radiology workforce of 2035.