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

Blair Edward Warren^1,2, Alexander Bilbily^1,3,4, Judy Wawira Gichoya⁵, Aaron Conway⁶, Ben Li⁷, Aly Fawzy¹, Camilo Barragán^1,2, Arash Jaberi^1,2, Sebastian Mafeld^1,2

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

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

³16 Bit Inc., Toronto, ON, Canada.

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

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

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

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

PubMed DOI

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 o…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.