An Introductory Guide to Artificial Intelligence in Interventional Radiology: Part 1 Foundational Knowledge.

Journal: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Published Date: Mar 6, 2024

Abstract

Artificial intelligence (AI) is rapidly evolving and has transformative potential for interventional radiology (IR) clinical practice. However, formal training in AI may be limited for many clinicians and therefore presents a challenge for initial implementation and trust in AI. An understanding of the foundational concepts in AI may help familiarize the interventional radiologist with the field of AI, thus facilitating understanding and participation in the development and deployment of AI. A pragmatic classification system of AI based on the complexity of the model may guide clinicians in the assessment of AI. Finally, the current state of AI in IR and the patterns of implementation are explored (pre-procedural, intra-procedural, and post-procedural).

Authors

  • Blair Edward Warren
    Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Alexander Bilbily
  • Judy Wawira Gichoya
    Department of Interventional Radiology, Oregon Health & Science University, Portland, Oregon; Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia.
  • Aaron Conway
    Peter Munk Cardiac Centre, University Health Network, Toronto, Canada. aaron.conway@utoronto.ca.
  • Ben Li
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China. Electronic address: LBen@sxmu.edu.cn.
  • Aly Fawzy
    Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Camilo Barragán
    Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Arash Jaberi
    Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.
  • Sebastian Mafeld
    Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada.

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