Proceedings from the Society of Interventional Radiology Foundation Research Consensus Panel on Artificial Intelligence in Interventional Radiology: From Code to Bedside.


Journal

Journal of vascular and interventional radiology : JVIR
ISSN: 1535-7732
Titre abrégé: J Vasc Interv Radiol
Pays: United States
ID NLM: 9203369

Informations de publication

Date de publication:
09 2022
Historique:
received: 06 05 2022
revised: 02 06 2022
accepted: 04 06 2022
pubmed: 24 7 2022
medline: 9 9 2022
entrez: 23 7 2022
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 35871021
pii: S1051-0443(22)00985-X
doi: 10.1016/j.jvir.2022.06.003
pii:
doi:

Types de publication

Editorial Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1113-1120

Subventions

Organisme : FDA HHS
ID : U01 FD006936
Pays : United States

Informations de copyright

Copyright © 2022 SIR. Published by Elsevier Inc. All rights reserved.

Auteurs

Julius Chapiro (J)

Division of Vascular and Interventional Radiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut. Electronic address: julius.chapiro@yale.edu.

Bibb Allen (B)

Birmingham Radiological Group, Birmingham, Alabama; American College of Radiology, Reston, Virginia.

Aaron Abajian (A)

Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York.

Bradford Wood (B)

Center for Cancer Research, National Institutes of Health, Bethesda, Maryland.

Nishita Kothary (N)

Department of Radiology, Stanford Medicine, Palo Alto, California.

Dania Daye (D)

Mass General Imaging, Harvard, Boston, Massachusetts.

Harrison Bai (H)

Department of Radiology and Radiological Science, The Johns Hopkins Hospital, Baltimore, Maryland.

Art Sedrakyan (A)

Populational Health Sciences, Weill Cornell Medicine, New York City, New York.

Matthew Diamond (M)

Digital Health Center of Excellence, Food and Drug Administration, Washington, D.C.

Vahan Simonyan (V)

Department of Biochemistry and Molecular Medicine, George Washington School of Medicine and Health Sciences, Washington, D.C.

Gordon McLennan (G)

Department of Radiology, University of Colorado Anschutz Medical Center, Denver, Colorado.

Nadine Abi-Jaoudeh (N)

Department of Radiology, University of California Irvine, Irvine, California.

Bradley Pua (B)

Department of Radiology, Weill Cornell Medicine, New York City, New York.

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Classifications MeSH