Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep 22:12:955668.
doi: 10.3389/fonc.2022.955668. eCollection 2022.

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

Peng-Fei Lyu  1   2 , Yu Wang  1 , Qing-Xiang Meng  2 , Ping-Ming Fan  1 , Ke Ma  2 , Sha Xiao  3 , Xun-Chen Cao  2 , Guang-Xun Lin  4 , Si-Yuan Dong  5
Affiliations

Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis

Peng-Fei Lyu et al. Front Oncol. .

Abstract

Background: Artificial intelligence (AI) is more and more widely used in cancer, which is of great help to doctors in diagnosis and treatment. This study aims to summarize the current research hotspots in the Application of Artificial Intelligence in Cancer (AAIC) and to assess the research trends in AAIC.

Methods: Scientific publications for AAIC-related research from 1 January 1998 to 1 July 2022 were obtained from the Web of Science database. The metrics analyses using bibliometrics software included publication, keyword, author, journal, institution, and country. In addition, the blustering analysis on the binary matrix was performed on hot keywords.

Results: The total number of papers in this study is 1592. The last decade of AAIC research has been divided into a slow development phase (2013-2018) and a rapid development phase (2019-2022). An international collaboration centered in the USA is dedicated to the development and application of AAIC. Li J is the most prolific writer in AAIC. Through clustering analysis and high-frequency keyword research, it has been shown that AI plays a significantly important role in the prediction, diagnosis, treatment and prognosis of cancer. Classification, diagnosis, carcinogenesis, risk, and validation are developing topics. Eight hotspot fields of AAIC were also identified.

Conclusion: AAIC can benefit cancer patients in diagnosing cancer, assessing the effectiveness of treatment, making a decision, predicting prognosis and saving costs. Future AAIC research may be dedicated to optimizing AI calculation tools, improving accuracy, and promoting AI.

Keywords: ai; application; bibliometric analysis; cancer; research hotspots.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of data extraction of AAIC.
Figure 2
Figure 2
Line graph of information about AAIC documentation. (A) The number of articles and mean citations related to AAIC. (B) Main categories of AAIC-related articles. TC, Total citations.
Figure 3
Figure 3
The distribution and cooperation characteristics of countries.
Figure 4
Figure 4
The three-fields plot of authors, journals, and countries. AU, Author; UN, Unit; CO, Country.
Figure 5
Figure 5
The density map of citation visualization.
Figure 6
Figure 6
The co-occurrence map of keywords.
Figure 7
Figure 7
The thematic map of keywords.
Figure 8
Figure 8
Graphs of keyword growth and subject line trends. (A) The map of cumulative growth of core keywords over time; (B) The Graph of trend changes in subject terms over time.
Figure 9
Figure 9
The visualized mountain map of the keywords: Cluster 0: AI for diagnosis of gastric cancer and assessing tumor microenvironment; Cluster 1: AI for skin cancer diagnosis; Cluster 2: AI for assessing cancer prognosis; Cluster 3: AI models for assessing treatment response; Cluster 4: AI in lung cancer; Cluster 5:AI for Early Detection of Cancer; Cluster 6: AI for cancer prediction; Cluster 7: AI for breast cancer and cost analysis.
Figure 10
Figure 10
The visualized heat map linked to data matrix of keywords.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Shastry KA, Sanjay HA. Cancer diagnosis using artificial intelligence: A review. Artif Intell Rev (2022) 55(4):2641–73. doi: 10.1007/s10462-021-10074-4 - DOI
    1. Scotti V. Artificial intelligence. IEEE Instrum Meas Mag (2020) 23(3):27–31. doi: 10.1109/mim.2020.9082795 - DOI
    1. Guo YQ, Hao ZC, Zhao SC, Gong JQ, Yang F. Artificial intelligence in health care: Bibliometric analysis. J Med Internet Res (2020) 22(7):12. doi: 10.2196/18228 - DOI - PMC - PubMed
    1. Horie Y, Yoshio T, Aoyama K, Yoshimizu S, Horiuchi Y, Ishiyama A, et al. . Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc (2019) 89(1):25–32. doi: 10.1016/j.gie.2018.07.037 - DOI - PubMed
    1. Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. . Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol (2019) 103(2):167–75. doi: 10.1136/bjophthalmol-2018-313173 - DOI - PMC - PubMed

LinkOut - more resources