Exploring future directions for operational growth, imaging innovation and process augmentation in radiology

In this study, we explore key mechanisms and trends in radiology operations, imaging innovations and process augmentations to establish three hypotheses for the future of radiology, framed as a strategic outlook. The trend analysis is based on an international radiology survey on invest preferences, an analysis of five decades of imaging innovations since the 1980s, and an evaluation of the impact of recent AI advances on medical and radiological processes. The Operational Evolution hypothesis considers radiology operations going through three phases, from foundational setup through process optimization to systemic management of assets and results. The Imaging Innovation hypothesis is based on an evaluation of imaging innovations in CT, MRI, mammography and PET systems over the past five decades. The role of software increased significantly, with user-driven and automated device operations, speed of imaging procedure and accurate validated biomarkers for therapy decisions as the three key drivers. The Augmented Radiology hypothesis expects patient scheduling, the imaging procedure itself and reading and reporting processes to benefit from advances in health data integration and AI. Radiologists will be enabled to focus on critical cases and decisive medical decisions, while technicians are expected to be spending more time in actual patient care than device handling. As a limitation, the hypotheses only extrapolate market trends and technological advances and cannot predict the development of individual radiology organisations. Continuing and accelerating software, data and AI advances will shape operations, imaging procedures and radiology processes for a more human-centered radiology. • Established three hypotheses for the future of radiology based on key mechanisms and trends in radiology operations, imaging innovations and process augmentations. • Operational Evolution hypothesis considers radiology operations going through three phases, from foundation of operations over continuous process step optimization to systemic management of assets and results. • Imaging Innovation hypothesis predicts role of software increasing significantly, with user-driven and automated device operations, speed of imaging procedure and accurate validated biomarkers for therapy decisions as the three key drivers. • Augmented Radiology hypothesis expects Patient scheduling, the imaging procedure itself and reading and reporting to be significantly altered and improved by data integration and (generative) AI.