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Prime Time for Artificial Intelligence in Interventional Radiology
Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921296/ https://www.ncbi.nlm.nih.gov/pubmed/35031822 http://dx.doi.org/10.1007/s00270-021-03044-4 |
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author | Seah, Jarrel Boeken, Tom Sapoval, Marc Goh, Gerard S. |
author_facet | Seah, Jarrel Boeken, Tom Sapoval, Marc Goh, Gerard S. |
author_sort | Seah, Jarrel |
collection | PubMed |
description | Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI. |
format | Online Article Text |
id | pubmed-8921296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-89212962022-03-17 Prime Time for Artificial Intelligence in Interventional Radiology Seah, Jarrel Boeken, Tom Sapoval, Marc Goh, Gerard S. Cardiovasc Intervent Radiol Review Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI. Springer US 2022-01-14 2022 /pmc/articles/PMC8921296/ /pubmed/35031822 http://dx.doi.org/10.1007/s00270-021-03044-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Seah, Jarrel Boeken, Tom Sapoval, Marc Goh, Gerard S. Prime Time for Artificial Intelligence in Interventional Radiology |
title | Prime Time for Artificial Intelligence in Interventional Radiology |
title_full | Prime Time for Artificial Intelligence in Interventional Radiology |
title_fullStr | Prime Time for Artificial Intelligence in Interventional Radiology |
title_full_unstemmed | Prime Time for Artificial Intelligence in Interventional Radiology |
title_short | Prime Time for Artificial Intelligence in Interventional Radiology |
title_sort | prime time for artificial intelligence in interventional radiology |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921296/ https://www.ncbi.nlm.nih.gov/pubmed/35031822 http://dx.doi.org/10.1007/s00270-021-03044-4 |
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