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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| Published in | Cancer science Vol. 111; no. 5; pp. 1452 - 1460 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
John Wiley & Sons, Inc
01.05.2020
John Wiley and Sons Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1347-9032 1349-7006 1349-7006 |
| DOI | 10.1111/cas.14377 |
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| Abstract | 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.
Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. 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. |
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| AbstractList | 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.
Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. 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. 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.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. 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. |
| Author | Shimizu, Hideyuki Nakayama, Keiichi I. |
| AuthorAffiliation | 1 Department of Molecular and Cellular Biology Medical Institute of Bioregulation Kyushu University Fukuoka Japan |
| AuthorAffiliation_xml | – name: 1 Department of Molecular and Cellular Biology Medical Institute of Bioregulation Kyushu University Fukuoka Japan |
| Author_xml | – sequence: 1 givenname: Hideyuki surname: Shimizu fullname: Shimizu, Hideyuki organization: Kyushu University – sequence: 2 givenname: Keiichi I. orcidid: 0000-0002-7185-1529 surname: Nakayama fullname: Nakayama, Keiichi I. email: nakayak1@bioreg.kyushu-u.ac.jp organization: Kyushu University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32133724$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 The Authors. published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. 2020 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. 2020. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | deep learning machine learning artificial intelligence oncology personalized medicine |
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| SubjectTerms | Algorithms Artificial intelligence Cancer Deep learning Dermatology Industrialized nations Machine learning Mammography Medical research Medical screening Neural networks Oncology Pathology personalized medicine Researchers Review |
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