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Artificial intelligence in oncology

Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas...

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Detalles Bibliográficos
Autores principales: Shimizu, Hideyuki, Nakayama, Keiichi I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226189/
https://www.ncbi.nlm.nih.gov/pubmed/32133724
http://dx.doi.org/10.1111/cas.14377
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author Shimizu, Hideyuki
Nakayama, Keiichi I.
author_facet Shimizu, Hideyuki
Nakayama, Keiichi I.
author_sort Shimizu, Hideyuki
collection PubMed
description Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade.
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spelling pubmed-72261892020-05-18 Artificial intelligence in oncology Shimizu, Hideyuki Nakayama, Keiichi I. Cancer Sci Review Articles Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade. John Wiley and Sons Inc. 2020-03-21 2020-05 /pmc/articles/PMC7226189/ /pubmed/32133724 http://dx.doi.org/10.1111/cas.14377 Text en © 2020 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Review Articles
Shimizu, Hideyuki
Nakayama, Keiichi I.
Artificial intelligence in oncology
title Artificial intelligence in oncology
title_full Artificial intelligence in oncology
title_fullStr Artificial intelligence in oncology
title_full_unstemmed Artificial intelligence in oncology
title_short Artificial intelligence in oncology
title_sort artificial intelligence in oncology
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226189/
https://www.ncbi.nlm.nih.gov/pubmed/32133724
http://dx.doi.org/10.1111/cas.14377
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