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

Blair E. Warren, Alexander Bilbily, Judy Wawira Gichoya, Aaron Conway, Ben Li, Aly Fawzy, Camilo Barragán, Arash Jaberi, Sebastian Mafeld

2024Canadian Association of Radiologists Journal11 citationsDOIOpen Access PDF

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).

Topics & Concepts

MedicineTransformative learningSoftware deploymentRadiologyArtificial intelligenceApplications of artificial intelligenceField (mathematics)Computer scienceSoftware engineeringPsychologyMathematicsPure mathematicsPedagogyArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical ImagingRadiology practices and education
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