Artificial intelligence in clinical research of cancers

Abstract 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....

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Published inBriefings in bioinformatics Vol. 23; no. 1
Main Authors Shao, Dan, Dai, Yinfei, Li, Nianfeng, Cao, Xuqing, Zhao, Wei, Cheng, Li, Rong, Zhuqing, Huang, Lan, Wang, Yan, Zhao, Jing
Format Journal Article
LanguageEnglish
Published England Oxford University Press 17.01.2022
Oxford Publishing Limited (England)
Subjects
Algorithms
Artificial Intelligence
Cancer
Cancer research
Computer applications
Databases, Factual
Deep Learning
Domains
Genomes
Humans
Medical records
Medical research
Neoplasms - genetics
Research Design
Review
deep learning
drug discovery
clinical research of cancers
artificial intelligence
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Summary:Abstract 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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ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbab523