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Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology
Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000832/ https://www.ncbi.nlm.nih.gov/pubmed/36900036 http://dx.doi.org/10.3390/diagnostics13050892 |
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author | von Ende, Elizabeth Ryan, Sean Crain, Matthew A. Makary, Mina S. |
author_facet | von Ende, Elizabeth Ryan, Sean Crain, Matthew A. Makary, Mina S. |
author_sort | von Ende, Elizabeth |
collection | PubMed |
description | Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice. |
format | Online Article Text |
id | pubmed-10000832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100008322023-03-11 Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology von Ende, Elizabeth Ryan, Sean Crain, Matthew A. Makary, Mina S. Diagnostics (Basel) Review Artificial intelligence (AI) uses computer algorithms to process and interpret data as well as perform tasks, while continuously redefining itself. Machine learning, a subset of AI, is based on reverse training in which evaluation and extraction of data occur from exposure to labeled examples. AI is capable of using neural networks to extract more complex, high-level data, even from unlabeled data sets, and better emulate, or even exceed, the human brain. Advances in AI have and will continue to revolutionize medicine, especially the field of radiology. Compared to the field of interventional radiology, AI innovations in the field of diagnostic radiology are more widely understood and used, although still with significant potential and growth on the horizon. Additionally, AI is closely related and often incorporated into the technology and programming of augmented reality, virtual reality, and radiogenomic innovations which have the potential to enhance the efficiency and accuracy of radiological diagnoses and treatment planning. There are many barriers that limit the applications of artificial intelligence applications into the clinical practice and dynamic procedures of interventional radiology. Despite these barriers to implementation, artificial intelligence in IR continues to advance and the continued development of machine learning and deep learning places interventional radiology in a unique position for exponential growth. This review describes the current and possible future applications of artificial intelligence, radiogenomics, and augmented and virtual reality in interventional radiology while also describing the challenges and limitations that must be addressed before these applications can be fully implemented into common clinical practice. MDPI 2023-02-27 /pmc/articles/PMC10000832/ /pubmed/36900036 http://dx.doi.org/10.3390/diagnostics13050892 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review von Ende, Elizabeth Ryan, Sean Crain, Matthew A. Makary, Mina S. Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology |
title | Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology |
title_full | Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology |
title_fullStr | Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology |
title_full_unstemmed | Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology |
title_short | Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology |
title_sort | artificial intelligence, augmented reality, and virtual reality advances and applications in interventional radiology |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000832/ https://www.ncbi.nlm.nih.gov/pubmed/36900036 http://dx.doi.org/10.3390/diagnostics13050892 |
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