Publication Name	World Journal of Clinical Cases
Manuscript ID	93409
Country	Qatar

Received	2024-02-26 17:35
Peer-Review Started	2024-02-26 17:35
To Make the First Decision
Return for Revision	2024-04-04 03:45
Revised	2024-04-04 23:11
Second Decision	2024-04-18 02:37
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-04-18 07:47
Articles in Press	2024-04-18 07:47
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-05-27 15:44
Publish the Manuscript Online	2024-06-04 15:55

ISSN	2307-8960 (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.
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Category	Methodology
Manuscript Type	Editorial
Article Title	Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification
Manuscript Source	Invited Manuscript
All Author List	Jaber H Jaradat and Abdulqadir J Nashwan
Funding Agency and Grant Number
Corresponding Author	Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing , Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Key Words	Liver diseases; Magnetic resonance imaging; Iron quantification; Machine learning; Deep learning
Core Tip	This editorial emphasizes the revolutionary impact of artificial intelligence (AI), particularly machine learning and deep learning techniques like convolutional neural networks (CNNs), in healthcare. Highlighting the limitations of traditional, slightly invasive blood tests for assessing body iron load, it advocates for magnetic resonance imaging 's non-invasive advantages. The editorial underscores the critical role of AI and CNNs in improving disease diagnosis and treatment, especially through the precise detection of minor changes in liver iron levels. However, it points out the significant hurdle of lacking open-access datasets, which hampers medical research progress. The call for standardized datasets is a crucial step towards leveraging AI in medical imaging, promising to transform patient care and management.
Publish Date	2024-06-04 15:55
Citation	<p>Jaradat JH, Nashwan AJ. Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification. <i>World J Clin Cases</i> 2024; 12(17): 2921-2924</p>
URL	https://www.wjgnet.com/2307-8960/full/v12/i17/2921.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i17.2921

Full Article (PDF)	WJCC-12-2921-with-cover.pdf
Full Article (Word)	WJCC-12-2921.docx
Manuscript File	93409_Auto_Edited-YJP.docx
Answering Reviewers	93409-answering-reviewers.pdf
Audio Core Tip	93409-audio.mp3
Conflict-of-Interest Disclosure Form	93409-conflict-of-interest-statement.pdf
Copyright License Agreement	93409-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	93409-non-native-speakers.pdf
Peer-review Report	93409-peer-reviews.pdf
Scientific Misconduct Check	93409-scientific-misconduct-check.png
Scientific Editor Work List	93409-scientific-editor-work-list.pdf
CrossCheck Report	93409-crosscheck-report.pdf