Evidence map›Paper›PMID 39185394›Full record

ArticleInternational journal of chronic obstructive pulmonary disease2024

Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023.

Hupo Bian, Shaoqi Zhu, Yonghua Zhang, Qiang Fei, Xiuhua Peng, Zanhui Jin, Tianxiang Zhou, Hongxing Zhao

Abstract read
In one paragraph

Article in International journal of chronic obstructive pulmonary disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. PulmoClass-3DAtt: A Self-Attention Network for Classification of COPD, PRISm and Normal.International journal of chronic obstructive pulmonary disease · 2026
    Article
  7. Review
  8. Review
  9. Review
  10. Article
  11. Article
  12. Review
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

8 authors.

Hupo BianDepartment of Radiology, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.ORCID 0009-0002-6857-1611
Shaoqi ZhuDepartment of Endocrinology, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.ORCID 0009-0009-4775-5471
Yonghua ZhangDepartment of Radiology, The Wuxing District People's Hospital, Huzhou, Zhejiang, People's Republic of China.ORCID 0009-0009-3099-7810
Qiang FeiDepartment of Radiology, The Linghu People's Hospital, Huzhou, Zhejiang, People's Republic of China.
Xiuhua PengDepartment of Radiology, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.
Zanhui JinDepartment of Radiology, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.
Tianxiang ZhouDepartment of Urinary Surgery, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.ORCID 0009-0005-5158-7141
Hongxing ZhaoDepartment of Radiology, The First Affiliated Hospital of Huzhou Normal University, Huzhou, Zhejiang, People's Republic of China.

Funding

Science and Technology Project of Huzhou City, Zhejiang Province 2023G Y33
6 · The paper itself

Abstract

Objective: A bibliometric analysis was conducted using VOSviewer and CiteSpace to examine studies published between 2009 and 2023 on the utilization of artificial intelligence (AI) in chronic obstructive pulmonary disease (COPD). Methods: On March 24, 2024, a computer search was conducted on the Web of Science (WOS) core collection dataset published between January 1, 2009, and December 30, 2023, to identify literature related to the application of artificial intelligence in chronic obstructive pulmonary disease (COPD). VOSviewer was utilized for visual analysis of countries, institutions, authors, co-cited authors, and keywords. CiteSpace was employed to analyze the intermediary centrality of institutions, references, keyword outbreaks, and co-cited literature. Relevant descriptive analysis tables were created using Excel2021 software. Results: This study included a total of 646 papers from WOS. The number of papers remained small and stable from 2009 to 2017 but started increasing significantly annually since 2018. The United States had the highest number of publications among countries/regions while Silverman Edwin K and Harvard Medical School were the most prolific authors and institutions respectively. Lynch DA, Kirby M. and Vestbo J. were among the top three most cited authors overall. Scientific Reports had the largest number of publications while Radiology ranked as one of the top ten influential journals. The Genetic Epidemiology of COPD (COPDGene) Study Design was frequently cited. Through keyword clustering analysis, all keywords were categorized into four groups: epidemiological study of COPD; AI-assisted imaging diagnosis; AI-assisted diagnosis; and AI-assisted treatment and prognosis prediction in the COPD research field. Currently, hot research topics include explainable artificial intelligence framework, chest CT imaging, and lung radiomics. Conclusion: At present, AI is predominantly employed in genetic biology, early diagnosis, risk staging, efficacy evaluation, and prediction modeling of COPD. This study's results offer novel insights and directions for future research endeavors related to COPD.

Indexed as

Artificial IntelligenceBibliometricsBiomedical ResearchPulmonary Disease, Chronic ObstructiveDiffusion of InnovationForecastingHumansTime Factorsartificial intelligencebibliometric analysischronic obstructive pulmonary diseasevisual analysis

Identifiers

PMID39185394
PMCPMC11345018

What OpenQuestion holds

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Registered trials

None linked

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.