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Artificial intelligence and machine learning in cancer imaging
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and tes...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613681/ https://www.ncbi.nlm.nih.gov/pubmed/36310650 http://dx.doi.org/10.1038/s43856-022-00199-0 |
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author | Koh, Dow-Mu Papanikolaou, Nickolas Bick, Ulrich Illing, Rowland Kahn, Charles E. Kalpathi-Cramer, Jayshree Matos, Celso Martí-Bonmatí, Luis Miles, Anne Mun, Seong Ki Napel, Sandy Rockall, Andrea Sala, Evis Strickland, Nicola Prior, Fred |
author_facet | Koh, Dow-Mu Papanikolaou, Nickolas Bick, Ulrich Illing, Rowland Kahn, Charles E. Kalpathi-Cramer, Jayshree Matos, Celso Martí-Bonmatí, Luis Miles, Anne Mun, Seong Ki Napel, Sandy Rockall, Andrea Sala, Evis Strickland, Nicola Prior, Fred |
author_sort | Koh, Dow-Mu |
collection | PubMed |
description | An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging. |
format | Online Article Text |
id | pubmed-9613681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96136812022-10-29 Artificial intelligence and machine learning in cancer imaging Koh, Dow-Mu Papanikolaou, Nickolas Bick, Ulrich Illing, Rowland Kahn, Charles E. Kalpathi-Cramer, Jayshree Matos, Celso Martí-Bonmatí, Luis Miles, Anne Mun, Seong Ki Napel, Sandy Rockall, Andrea Sala, Evis Strickland, Nicola Prior, Fred Commun Med (Lond) Review Article An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging. Nature Publishing Group UK 2022-10-27 /pmc/articles/PMC9613681/ /pubmed/36310650 http://dx.doi.org/10.1038/s43856-022-00199-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article Koh, Dow-Mu Papanikolaou, Nickolas Bick, Ulrich Illing, Rowland Kahn, Charles E. Kalpathi-Cramer, Jayshree Matos, Celso Martí-Bonmatí, Luis Miles, Anne Mun, Seong Ki Napel, Sandy Rockall, Andrea Sala, Evis Strickland, Nicola Prior, Fred Artificial intelligence and machine learning in cancer imaging |
title | Artificial intelligence and machine learning in cancer imaging |
title_full | Artificial intelligence and machine learning in cancer imaging |
title_fullStr | Artificial intelligence and machine learning in cancer imaging |
title_full_unstemmed | Artificial intelligence and machine learning in cancer imaging |
title_short | Artificial intelligence and machine learning in cancer imaging |
title_sort | artificial intelligence and machine learning in cancer imaging |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613681/ https://www.ncbi.nlm.nih.gov/pubmed/36310650 http://dx.doi.org/10.1038/s43856-022-00199-0 |
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