Evidence map›Paper›PMID 34929741›Full record

ArticleBriefings in bioinformatics2022

Artificial intelligence in clinical research of cancers.

Dan Shao, Yinfei Dai, Nianfeng Li, Xuqing Cao, Wei Zhao, Li Cheng, Zhuqing Rong, Lan Huang, Yan Wang, Jing Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Dan ShaoCollege of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China.ORCID 0000-0003-1050-2808
Yinfei DaiCollege of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China.
Nianfeng LiCollege of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China.
Xuqing CaoDepartment of Neurology, People's Hospital of Ningxia Hui Autonomous Region (The Affiliated people's Hospital of Ningxia Medical University and The First Affiliated Hospital of Northwest Minzu University), Yinchuan 750002, China.
Wei ZhaoDepartment of Biochemistry and Molecular Biology, Ningxia Medical University, Yinchuan 750002, China.
Li ChengDepartment of Electrical Diagnosis, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, 130021, China.
Zhuqing RongSchool of Science, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China.
Lan HuangKey laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Yan WangKey laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.ORCID 0000-0002-4751-0708
Jing ZhaoDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, 43210, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceDeep LearningNeoplasmsAlgorithmsDatabases, FactualHumansResearch Designartificial intelligenceclinical research of cancersdeep learningdrug discovery

Identifiers

PMID34929741
PMCPMC8769909

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.