Call to Action: Accelerating AI-driven Transformation in Medical Imaging and the Broader Health Care System.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, NYU Grossman School of Medicine, Center for Advanced Imaging Innovation and Research, 560 First Ave, New York, NY 10016.
- Department of Radiology, NYU Grossman School of Medicine, New York, NY.
- Department of Radiology & Imaging Sciences, Emory University, Atlanta, Ga.
- Department of Radiology, Stanford School of Medicine, Microsoft Health & Life Sciences, Stanford, Calif.
- Department of Medical Imaging, Hospital Universitario y Politécnico La Fe, Valencia, Spain.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif.
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa.
Abstract
Global imaging demand now exceeds capacity due to an aging population, rising comorbidities, expanded indications for imaging, and workforce shortages. These pressures are reshaping national health care policy. At the same time, the competing demands to expand access to imaging services while reducing costs are creating momentum to reimagine imaging delivery through the integration of transformative technologies like artificial intelligence (AI). This consensus statement from the International Society for Strategic Studies in Radiology (IS3R) is a call to action, outlining promising AI solutions that increase workflow efficiency and align radiology with health system and payer priorities. Reflecting expert consensus from the IS3R meeting in Dublin, Ireland, in August 2025, this statement defines strategic directions for radiology amid accelerating digitalization, rapid innovation, and data-driven patient care. This article covers four domains: image interpretation, capacity building, data governance, and systemic digital transformation. This article examines how AI can address rising imaging demand and costs while addressing challenges related to improving data quality, standardization, and interoperability. It also highlights the need to re-engineer workflows and adapt to new care models while maintaining high-quality services. Finally, it presents consensus recommendations outlining how AI can enable multimodal health data integration and advance alignment with value-based care.