Publication Name	World Journal of Methodology
Manuscript ID	99403
Country	Jordan

Received	2024-08-22 08:45
Peer-Review Started	2024-07-21 23:36
To Make the First Decision
Return for Revision	2024-11-22 12:25
Revised	2024-12-03 03:45
Second Decision	2024-12-23 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-12-23 08:45
Articles in Press	2024-12-23 08:45
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-12-30 04:42
Publish the Manuscript Online	2025-03-06 10:23

ISSN	2222-0682 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions	For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher	Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website	http://www.wjgnet.com

Category	Neurosciences
Manuscript Type	Scientometrics
Article Title	Use of artificial intelligence in neurological disorders diagnosis: A scientometric study
Manuscript Source	Invited Manuscript
All Author List	Alaa Tarazi, Ahmad Aburrub and Mohammad Hijah
ORCID	Author(s) ORCID Number Alaa Tarazi http://orcid.org/0009-0002-8483-483X
Funding Agency and Grant Number
Corresponding Author	Alaa Tarazi, MD, School of Medicine, University of Jordan, Queen Rania St, Amman 11942, Jordan. alaatarazi11@gmail.com
Key Words	Artificial intelligence; Machine learning; Neurological disorders; Diagnosis; Bibliometric analysis
Core Tip	Artificial intelligence's (AI) role in diagnosing neurological disorders has been increasingly recognized. We conducted a scientometric analysis to explore the current status of articles in this field and identify the most prolific contributors from various perspectives. Johns Hopkins University in the United States emerged as the leading institution, with the United States also leading overall productivity in this field. IEEE Access was noted as the top journal. Research highlights AI's effectiveness in diagnosing diverse neurological disorders, offering significant benefits for patients and healthcare providers. Continued advancements are expected in AI's role in neurological disorder diagnosis.
Publish Date	2025-03-06 10:23
Citation	<p>Tarazi A, Aburrub A, Hijah M. Use of artificial intelligence in neurological disorders diagnosis: A scientometric study. <i>World J Methodol</i> 2025; 15(3): 99403</p>
URL	https://www.wjgnet.com/2222-0682/full/v15/i3/99403.htm
DOI	https://dx.doi.org/10.5662/wjm.v15.i3.99403

Full Article (PDF)	WJM-15-99403-with-cover.pdf
PRISMA 2009 Checklist	99403-PRISMA-2009-Checklist.pdf
Manuscript File	99403_Auto_Edited_102033.docx
Answering Reviewers	99403-answering-reviewers.pdf
Audio Core Tip	99403-audio.m4a
Biostatistics Review Certificate	99403-biostatistics-statement.pdf
Conflict-of-Interest Disclosure Form	99403-conflict-of-interest-statement.pdf
Copyright License Agreement	99403-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	99403-non-native-speakers.pdf
Peer-review Report	99403-peer-reviews.pdf
Scientific Misconduct Check	99403-scientific-misconduct-check.png
Scientific Editor Work List	99403-scientific-editor-work-list.pdf
CrossCheck Report	99403-crosscheck-report.pdf