| Literature DB >> 35005333 |
Rohil Malpani1, Christopher W Petty1, Neha Bhatt1, Lawrence H Staib1, Julius Chapiro1.
Abstract
The future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in non-oncologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, post-procedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current 'black box' of AI research and help bridge the gap between the research laboratory and clinical practice.Entities:
Keywords: artificial intelligence; deep learning; interventional radiology; machine learning; radiomics
Year: 2021 PMID： 35005333 PMCID： PMC8740955 DOI： 10.1055/s-0041-1726300
Source DB: PubMed Journal: Dig Dis Interv ISSN： 2472-8721