Prime Time for Artificial Intelligence in Interventional Radiology.

Journal: Cardiovascular and interventional radiology
Published Date: Jan 14, 2022

Abstract

Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.

Authors

  • Jarrel Seah
    Department of Neuroscience, Monash University, Melbourne, Australia; Radiology and Nuclear Medicine, Alfred Health, Melbourne, Australia.
  • Tom Boeken
    Vascular and Oncological Interventional Radiology, University of Paris, Hopital Européen Georges Pompidou, Paris, France.
  • Marc Sapoval
    Vascular and Oncological Interventional Radiology, University of Paris, Hopital Européen Georges Pompidou, Paris, France.
  • Gerard S Goh
    Department of Radiology, Alfred Health, Melbourne, VIC, Australia. g.goh@alfred.org.au.

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