Publication Name	World Journal of Clinical Cases
Manuscript ID	101306
Country	Malaysia

Received	2024-09-10 11:46
Peer-Review Started	2024-09-10 11:46
To Make the First Decision
Return for Revision	2024-09-25 06:34
Revised	2024-10-09 05:18
Second Decision	2024-11-05 02:45
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-11-05 10:54
Articles in Press	2024-11-05 10:54
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-11-12 00:32
Publish the Manuscript Online	2024-11-19 06:00

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	Endocrinology & Metabolism
Manuscript Type	Editorial
Article Title	Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning
Manuscript Source	Invited Manuscript
All Author List	Mona Mohamed Ibrahim Abdalla and Jaiprakash Mohanraj
Funding Agency and Grant Number
Corresponding Author	Mona Mohamed Ibrahim Abdalla, MD, PhD, Department of Human Biology, School of Medicine, International Medical University, No. 126 Jln Jalil Perkasa 19, Bukit Jalil 57000, Kuala Lumpur, Malaysia. monamohamed@imu.edu.my
Key Words	Diabetic retinopathy; Artificial intelligence; Machine learning; Screening; Management; Predictive analytics; Personalized medicine
Core Tip	Leveraging artificial intelligence (AI) and machine learning in diabetic retinopathy care can significantly enhance early detection and personalized treatment. Clinicians should embrace AI-driven screening tools that analyze retinal images with high precision, reducing the risk of human error and improving diagnostic accuracy. Implementing predictive analytics can help in identifying patients at higher risk, allowing for timely interventions and tailored treatment plans. To maximize the benefits, healthcare systems must invest in training and integrating these technologies seamlessly into clinical workflows. Collaborations between technologists and healthcare providers are crucial for developing robust, ethical, and equitable AI solutions in ophthalmic care.
Publish Date	2024-11-19 06:00
Citation	<p>Abdalla MMI, Mohanraj J. Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning. <i>World J Clin Cases</i> 2025; 13(5): 101306</p>
URL	https://www.wjgnet.com/2307-8960/full/v13/i5/101306.htm
DOI	https://dx.doi.org/10.12998/wjcc.v13.i5.101306

Full Article (PDF)	WJCC-13-101306-with-cover.pdf
Manuscript File	101306_Auto_Edited_054853.docx
Answering Reviewers	101306-answering-reviewers.pdf
Audio Core Tip	101306-audio.mp3
Conflict-of-Interest Disclosure Form	101306-conflict-of-interest-statement.pdf
Copyright License Agreement	101306-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	101306-non-native-speakers.pdf
Peer-review Report	101306-peer-reviews.pdf
Scientific Misconduct Check	101306-scientific-misconduct-check.png
Scientific Editor Work List	101306-scientific-editor-work-list.pdf
CrossCheck Report	101306-crosscheck-report.png
CrossCheck Report	101306-crosscheck-report.pdf