Abdalla MMI, Mohanraj J. Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning. World J Clin Cases 2025; 13(5): 101306 [DOI: 10.12998/wjcc.v13.i5.101306]
Corresponding Author of This Article
Mona Mohamed Ibrahim Abdalla, MD, MSc, PhD, Doctor, Senior Lecturer, 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
Research Domain of This Article
Endocrinology & Metabolism
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Feb 16, 2025; 13(5): 101306 Published online Feb 16, 2025. doi: 10.12998/wjcc.v13.i5.101306
Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning
Mona Mohamed Ibrahim Abdalla, Jaiprakash Mohanraj
Mona Mohamed Ibrahim Abdalla, Jaiprakash Mohanraj, Department of Human Biology, School of Medicine, International Medical University, Bukit Jalil 57000, Kuala Lumpur, Malaysia
Co-first authors: Mona Mohamed Ibrahim Abdalla and Jaiprakash Mohanraj.
Author contributions: Abdalla MMI and Mohanraj J contribute equally to this article as co-first authors. Abdalla MMI and Mohanraj J performed the research and wrote the paper.
Conflict-of-interest statement: The authors declare no conflict of interest to this paper.
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/
Corresponding author: Mona Mohamed Ibrahim Abdalla, MD, MSc, PhD, Doctor, Senior Lecturer, 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
Received: September 10, 2024 Revised: October 9, 2024 Accepted: November 5, 2024 Published online: February 16, 2025 Processing time: 69 Days and 18.6 Hours
Abstract
Diabetic retinopathy (DR) remains a leading cause of vision impairment and blindness among individuals with diabetes, necessitating innovative approaches to screening and management. This editorial explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing DR care. AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy, efficiency, and accessibility of DR screening, helping to overcome barriers to early detection. These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision, enabling clinicians to make more informed decisions. Furthermore, AI-driven solutions hold promise in personalizing management strategies for DR, incorporating predictive analytics to tailor interventions and optimize treatment pathways. By automating routine tasks, AI can reduce the burden on healthcare providers, allowing for a more focused allocation of resources towards complex patient care. This review aims to evaluate the current advancements and applications of AI and ML in DR screening, and to discuss the potential of these technologies in developing personalized management strategies, ultimately aiming to improve patient outcomes and reduce the global burden of DR. The integration of AI and ML in DR care represents a paradigm shift, offering a glimpse into the future of ophthalmic healthcare.
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.
