Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
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
_version_ 1784820028511617024
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
work_keys_str_mv AT kohdowmu artificialintelligenceandmachinelearningincancerimaging
AT papanikolaounickolas artificialintelligenceandmachinelearningincancerimaging
AT bickulrich artificialintelligenceandmachinelearningincancerimaging
AT illingrowland artificialintelligenceandmachinelearningincancerimaging
AT kahncharlese artificialintelligenceandmachinelearningincancerimaging
AT kalpathicramerjayshree artificialintelligenceandmachinelearningincancerimaging
AT matoscelso artificialintelligenceandmachinelearningincancerimaging
AT martibonmatiluis artificialintelligenceandmachinelearningincancerimaging
AT milesanne artificialintelligenceandmachinelearningincancerimaging
AT munseongki artificialintelligenceandmachinelearningincancerimaging
AT napelsandy artificialintelligenceandmachinelearningincancerimaging
AT rockallandrea artificialintelligenceandmachinelearningincancerimaging
AT salaevis artificialintelligenceandmachinelearningincancerimaging
AT stricklandnicola artificialintelligenceandmachinelearningincancerimaging
AT priorfred artificialintelligenceandmachinelearningincancerimaging