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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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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. |
format | Online Article Text |
id | pubmed-8769909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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