Publication Name	World Journal of Clinical Cases
Manuscript ID	99744
Country	Greece

Received	2024-08-06 06:49
Peer-Review Started	2024-08-06 06:49
To Make the First Decision
Return for Revision	2024-09-15 03:13
Revised	2024-09-25 17:25
Second Decision	2024-10-14 02:40
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-10-15 05:43
Articles in Press	2024-10-15 05:43
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-10-29 16:25
Publish the Manuscript Online	2024-11-07 05:10

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/
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Category	Medicine, Research & Experimental
Manuscript Type	Editorial
Article Title	Machine learning applications in healthcare clinical practice and research
Manuscript Source	Invited Manuscript
All Author List	Nikolaos-Achilleas Arkoudis and Stavros P Papadakos
ORCID	Author(s) ORCID Number Nikolaos-Achilleas Arkoudis http://orcid.org/0000-0002-0783-5700 Stavros P Papadakos http://orcid.org/0000-0003-1583-1125
Funding Agency and Grant Number
Corresponding Author	Nikolaos-Achilleas Arkoudis, MD, PhD, Research Unit of Radiology and Medical Imaging, School of Medicine, National and Kapodistrian University of Athens, 19 Papadiamantopoulou, Athens 11528, Greece. nick.arkoudis@gmail.com
Key Words	Machine; Learning; Artificial; Intelligence; Clinical; Practice; Research; Glomerular filtration rate; Non-alcoholic fatty liver disease; Medicine
Core Tip	Across numerous diverse industries, machine learning (ML) is revolutionizing healthcare as well. It has demonstrated the potential to aid in disease diagnosis, treatment planning, decision-making, and outcome prediction, as well as improve clinical trial design and their success rates, often surpassing traditional methods. We highlight a study, published in the World Journal of Clinical Cases, where ML techniques proved superior to traditional statistical methods in analyzing factors affecting the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease.
Publish Date	2024-11-07 05:10
Citation	<p>Arkoudis NA, Papadakos SP. Machine learning applications in healthcare clinical practice and research. <i>World J Clin Cases</i> 2025; 13(1): 99744</p>
URL	https://www.wjgnet.com/2307-8960/full/v13/i1/99744.htm
DOI	https://dx.doi.org/10.12998/wjcc.v13.i1.99744

Full Article (PDF)	WJCC-13-99744-with-cover.pdf
Manuscript File	99744_Auto_Edited_141745.docx
Answering Reviewers	99744-answering-reviewers.pdf
Audio Core Tip	99744-audio.mp3
Conflict-of-Interest Disclosure Form	99744-conflict-of-interest-statement.pdf
Copyright License Agreement	99744-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	99744-non-native-speakers.pdf
Peer-review Report	99744-peer-reviews.pdf
Scientific Misconduct Check	99744-scientific-misconduct-check.png
Scientific Editor Work List	99744-scientific-editor-work-list.pdf
CrossCheck Report	99744-crosscheck-report.pdf