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Artificial intelligence in clinical research of cancers

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algori...

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Autores principales: Shao, Dan, Dai, Yinfei, Li, Nianfeng, Cao, Xuqing, Zhao, Wei, Cheng, Li, Rong, Zhuqing, Huang, Lan, Wang, Yan, Zhao, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769909/
https://www.ncbi.nlm.nih.gov/pubmed/34929741
http://dx.doi.org/10.1093/bib/bbab523
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author Shao, Dan
Dai, Yinfei
Li, Nianfeng
Cao, Xuqing
Zhao, Wei
Cheng, Li
Rong, Zhuqing
Huang, Lan
Wang, Yan
Zhao, Jing
author_facet Shao, Dan
Dai, Yinfei
Li, Nianfeng
Cao, Xuqing
Zhao, Wei
Cheng, Li
Rong, Zhuqing
Huang, Lan
Wang, Yan
Zhao, Jing
author_sort Shao, Dan
collection PubMed
description Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.
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spelling pubmed-87699092022-01-20 Artificial intelligence in clinical research of cancers Shao, Dan Dai, Yinfei Li, Nianfeng Cao, Xuqing Zhao, Wei Cheng, Li Rong, Zhuqing Huang, Lan Wang, Yan Zhao, Jing Brief Bioinform Review Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment. Oxford University Press 2021-12-21 /pmc/articles/PMC8769909/ /pubmed/34929741 http://dx.doi.org/10.1093/bib/bbab523 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Review
Shao, Dan
Dai, Yinfei
Li, Nianfeng
Cao, Xuqing
Zhao, Wei
Cheng, Li
Rong, Zhuqing
Huang, Lan
Wang, Yan
Zhao, Jing
Artificial intelligence in clinical research of cancers
title Artificial intelligence in clinical research of cancers
title_full Artificial intelligence in clinical research of cancers
title_fullStr Artificial intelligence in clinical research of cancers
title_full_unstemmed Artificial intelligence in clinical research of cancers
title_short Artificial intelligence in clinical research of cancers
title_sort artificial intelligence in clinical research of cancers
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769909/
https://www.ncbi.nlm.nih.gov/pubmed/34929741
http://dx.doi.org/10.1093/bib/bbab523
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