Evidence map›Paper›PMID 40438502›Full record

ArticleFrontiers in aging neuroscience2025

Knowledge map of artificial intelligence in neurodegenerative diseases: a decade-long bibliometric and visualization study.

Junwei Huang, Shuqi Wang, Xuankai Liao, Danting Su, Rubing Lin, Tao Zhang, Long Zhao

Abstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

7 authors.

Junwei Huang *Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao, China.
Shuqi Wang *Department of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Xuankai LiaoDepartment of Pathology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
Danting SuDepartment of Pathology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
Rubing LinDepartment of Orthopedics, Shenzhen Children's Hospital, Shenzhen, China.
Tao ZhangDepartment of Neurosurgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Long ZhaoDepartment of Neurosurgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As the incidence of neurodegenerative diseases increases, the related AI research is getting more and more advanced. In this study, we analyze the literature in this field over the last decade through bibliometric and visualization methods with the aim of mining the prominent journals, institutions, authors, and countries in this field and analyzing the keywords in order to speculate on possible future research trends. Methods: Our study extracted 1,921 relevant publications spanning 2015-2025 from the Web of Science Core Collection database. We conducted comprehensive bibliometric analyses and knowledge mapping visualizations using established scientometric tools: CiteSpace and Bibliometrix. Results: A total of 1921 documents were included in the study, the number of publications in this field showed an overall increasing trend, and the average number of citations showed a downward trend since 2019. Among the journals, Conclusion: Based on the relationship between keywords and time, we speculate that there are four possible research trends: 1. Precision diagnosis with multimodal data fusion. 2. Pathological mechanism analysis and target discovery. 3. Interpretable AI and clinical translation. 4. Technology differentiation for subdivided diseases.

Indexed as

artificial intelligencebibliometricsneurodegenerative diseasesParkinson’s diseaseWeb of Science

Identifiers

PMID40438502
PMCPMC12116524

What OpenQuestion holds

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LicenceCC BY
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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.