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Use of artificial intelligence in neurological disorders diagnosis: A scientometric study
Alaa Tarazi, Ahmad Aburrub, Mohammad Hijah. World Journal of Methodology, 2025-03-06
Topic: Risk, prevention and care in population studies
BACKGROUND Artificial intelligence (AI) has become significantly integrated into healthcare, particularly in the diagnosing of neurological disorders. This advancement has enabled neurologists and physicians to diagnose conditions more quickly and effectively, ultimately benefiting patients. AIM To explore the current status and key highlights of AI-related articles in diagnosing of neurological disorders. METHODS A systematic literature review was conducted in the Web of Science Core Collection database using the following strategy: TS = ("Artificial Intelligence" OR "Computational Intelligence" OR "Machine Learning" OR "AI") AND TS = ("Neurological disorders" OR "CNS disorder" AND "diagnosis"). The search was limited to articles and reviews. Microsoft Excel 2019 and VOSviewer were utilized to identify major contributors, including authors, institutions, countries, and journals. Additionally, VOSviewer was employed to analyze and visualize current trends and hot topics through network visualization maps. RESULTS A total of 276 publications from 2000 to 2024 were retrieved. The United States, India, and China emerged as the top contributors in this field. Major institutions included Johns Hopkins University, King's College London, and Harvard Medical School. The most prolific author was U. Rajendra Acharya from the University of Southern Queensland (Australia). Among journals, IEEE Access , Scientific Reports , and Sensors were the most productive, while Frontiers in Neuroscience led in total citations. Central topics in AI-related articles on neurological disorders diagnosis included Alzheimer's disease, Parkinson's disease, dementia, epilepsy, autism, attention deficit hyperactivity disorder, and their intersections with deep learning and AI. CONCLUSION Research on AI's role in diagnosing neurological disorders is becoming widely recognized for its growing importance. AI shows promise in diagnosing various neurological disorders, yet requires further improvement and extensive future research.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Tarazi, A., Aburrub, A., & Hijah, M. (2025). Use of artificial intelligence in neurological disorders diagnosis: A scientometric study. World Journal of Methodology. https://doi.org/10.5662/wjm.v15.i3.99403
Vancouver
Tarazi A, Aburrub A, Hijah M. Use of artificial intelligence in neurological disorders diagnosis: A scientometric study. World Journal of Methodology. 2025. doi:10.5662/wjm.v15.i3.99403. PMID: 40881219.
BibTeX
@article{tarazi2025-40881219,
author = {Tarazi, Alaa and Aburrub, Ahmad and Hijah, Mohammad},
title = {{Use of artificial intelligence in neurological disorders diagnosis: A scientometric study}},
journal = {World Journal of Methodology},
year = {2025},
doi = {10.5662/wjm.v15.i3.99403},
pmid = {40881219},
url = {https://doi.org/10.5662/wjm.v15.i3.99403}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- BACKGROUND Artificial intelligence (AI) has become significantly integrated into healthcare, particularly in the diagnosing of neurological disorders.
- RESULTS A total of 276 publications from 2000 to 2024 were retrieved.
Why it matters
This scientometric analysis of 276 publications describes the growth of artificial intelligence research in diagnosing neurological disorders. It maps contributors and trends but reports no findings on treating, curing, or preventing Alzheimer's disease.
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Study details
- Participants276
- Evidence strengthLow
How this study scored 34/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 33 of the 96 points we could judge it on, which is 34 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (World Journal of Methodology) |
| Journal's citation record | 0 / 4 | World Journal of Methodology: journal h-index 40 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 276 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Mohammad Hijah holds only 3 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | University of Jordan: institution h-index 276 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40881219) |
| Free to read | 2 / 2 | Free to read |
| Points | 33 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 33 of the 96 points we could judge it on is 34 out of 100. |
| Total | 34 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.5662/wjm.v15.i3.99403
- PubMed: 40881219
- Original source →
- Free full text: World Journal of Methodology →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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