AccScience Publishing / EJMO / Volume 5 / Issue 3 / DOI: 10.14744/ejmo.2021.24856
RESEARCH ARTICLE

Artificial Intelligence and Machine Learning in Oncology: Historical Overview of Documents Indexed in the Web of Science Database

Ibrahim Hussein Musa1 Ibrahim Zamit2 Marvellous Okeke3 Tosin Yinka Akintunde4,5 Taha Hussein Musa6,7
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1 School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China
2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
3 Department of Oncology, Southeast University School of Medicine, Dingjiaoqiao, Gulou District, Nanjing, China
4 Department of Sociology, Hohai University School of Public Administration, Nanjing, China
5 Department of Demography and Social Statistics, Obafemi Awolowo University, Osun State, Nigeria
6 Department of Epidemiology and Health Statistics, Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China
7 Biomedical Research Institute, Darfur College, Nyala, Sudan
EJMO 2021, 5(3), 239–248; https://doi.org/10.14744/ejmo.2021.24856
Submitted: 5 August 2021 | Accepted: 4 September 2021 | Published: 24 September 2021
© 2021 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Objectives: Artificial Intelligence (AI) and Machine Learning (ML) are innovations contributing to the diagnosis and treatment of cancer. The aim is to present an overview of AI and ML application in oncology research.

Methods: Data was retrieved from the web of science. Bibliometrics and R packages and VOSviwer software was used for mapping and network analysis.

Results: 214 publications were retrieved written by 1161 authors and published in 133 journals from 1988 to 2021. There has been a steadily increasing trend of research over the past years. AI and ML in oncology research have attracted the interest of the scientific community and the readership. The first ranked documents received a 173 citations score. It covers hot topics related to common mistakes in diagnostic classification in clinical and the potential future opportunities for precision oncology using AI. Aneja S and Thompson RF from the USA are the most productive author. Frontiers in Oncology is the most productive Journal. The United States is leading the research effort on the topics, followed by Korea. The collaboration and network between countries in AL or ML in oncology research were documented.

Conclusion: AI and ML in oncology research have attracted the interest of of the scientific community and readership. The trend of research has been steadily increasing globally.

Keywords
Artificial Intelligence
bibliometric analysis
machine learning
oncology
Conflict of interest
None declared.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing