| Affiliation: | 1. Division of Vascular and Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut;2. Birmingham Radiological Group, Birmingham, Alabama;3. American College of Radiology, Reston, Virginia;4. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York;5. Center for Cancer Research, National Institutes of Health, Bethesda, Maryland;6. Department of Radiology, Stanford Medicine, Palo Alto, California;7. Mass General Imaging, Harvard, Boston, Massachusetts;8. Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, Maryland;9. Populational Health Sciences, Weill Cornell Medicine, New York City, New York;10. Digital Health Center of Excellence, Food and Drug Administration, Washington, D.C.;11. Department of Biochemistry and Molecular Medicine, George Washington School of Medicine and Health Sciences, Washington, D.C.;12. Department of Radiology, University of Colorado Anschutz Medical Center, Denver, Colorado;13. Department of Radiology, University of California Irvine, Irvine, California;14. Department of Radiology, Weill Cornell Medicine, New York City, New York |
| Abstract: | Artificial intelligence (AI)-based technologies are the most rapidly growing field of innovation in healthcare with the promise to achieve substantial improvements in delivery of patient care across all disciplines of medicine. Recent advances in imaging technology along with marked expansion of readily available advanced health information, data offer a unique opportunity for interventional radiology (IR) to reinvent itself as a data-driven specialty. Additionally, the growth of AI-based applications in diagnostic imaging is expected to have downstream effects on all image-guidance modalities. Therefore, the Society of Interventional Radiology Foundation has called upon 13 key opinion leaders in the field of IR to develop research priorities for clinical applications of AI in IR. The objectives of the assembled research consensus panel were to assess the availability and understand the applicability of AI for IR, estimate current needs and clinical use cases, and assemble a list of research priorities for the development of AI in IR. Individual panel members proposed and all participants voted upon consensus statements to rank them according to their overall impact for IR. The results identified the top priorities for the IR research community and provide organizing principles for innovative academic-industrial research collaborations that will leverage both clinical expertise and cutting-edge technology to benefit patient care in IR. |
