Evidence map›Paper›PMID 38371616›Full record

ArticleFrontiers in oncology2024

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis.

Ruikang Zhong, Tangke Gao, Jinghua Li, Zexing Li, Xue Tian, Chi Zhang, Ximing Lin, Yuehui Wang, Lei Gao, Kaiwen Hu

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Mapping the evolution and frontiers ofTranslational lung cancer research · 2024
    Review
  4. Article
  5. Review
  6. Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
  7. Article
  8. The Application of Artificial Intelligence in Lung Cancer Research.Cancer control : journal of the Moffitt Cancer Center
    Article
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.

Ruikang Zhong *Beijing University of Chinese Medicine, Beijing, China.
Tangke Gao *Beijing University of Chinese Medicine, Beijing, China.
Jinghua LiBeijing University of Chinese Medicine, Beijing, China.
Zexing LiBeijing University of Chinese Medicine, Beijing, China.
Xue TianGuang'an Men Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Chi ZhangBeijing University of Chinese Medicine, Beijing, China.
Ximing LinBeijing University of Chinese Medicine, Beijing, China.
Yuehui WangBeijing University of Chinese Medicine, Beijing, China.
Lei GaoDongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Kaiwen HuDongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction. Objective: This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots. Results: A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Conclusions: Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.

Indexed as

artificial intelligencebibliometricsCitespacelung cancervisualizationVOSviewer

Identifiers

PMID38371616
PMCPMC10869611

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.