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

Author:

Warren Blair Edward12ORCID,Bilbily Alexander134,Gichoya Judy Wawira5,Conway Aaron6,Li Ben7,Fawzy Aly1,Barragán Camilo12ORCID,Jaberi Arash12,Mafeld Sebastian12

Affiliation:

1. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

2. Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada

3. 16 Bit Inc., Toronto, ON, Canada

4. Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada

5. Department of Radiology, Emory University, Atlanta, GA, USA

6. Prince Charles Hospital, Queensland University of Technology, Brisbane, QLD, Australia

7. Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada

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).

Publisher

SAGE Publications

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