Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Diabetes. Apr 15, 2026; 17(4): 117094
Published online Apr 15, 2026. doi: 10.4239/wjd.v17.i4.117094
Published online Apr 15, 2026. doi: 10.4239/wjd.v17.i4.117094
From pixels to precision: Artificial intelligence in diabetic eye disease screening and management
Francesco Cappellani, Caterina Gagliano, Fabiana D’Esposito, Department of Medicine and Surgery, University of Enna “Kore”, Enna 94100, Italy
Francesco Cappellani, Caterina Gagliano, Mediterranean Foundation, G.B. Morgagni, Catania 95125, Italy
Matteo Capobianco, Eye Clinic, Policlinico G. Rodolico, University of Catania, Catania 95121, Italy
Matteo Capobianco, Alessandro Avitabile, Faculty of Medicine, University of Catania, Catania 95123, Italy
Federico Visalli, Department of Ophthalmology, University of Catania, Catania 95123, Italy
Marieme Khouyyi, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina 98121, Italy
Mutali Musa, Department of Optometry, University of Benin, Benin 300283, Nigeria
Mutali Musa, Department of Ophthalmology, Africa Eye Laser Center Ltd, Benin 300211, Nigeria
Inferrera Leandro, Rosa Giglio, Daniele Tognetto, Marco Zeppieri, Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste 34129, Italy
Fabiana D’Esposito, Imperial College Ophthalmic Research Group Unit, Imperial College, London NW1 5QH, United Kingdom
Marco Zeppieri, Department of Ophthalmology, University Hospital of Udine, Udine 33100, Italy
Co-first authors: Francesco Cappellani and Matteo Capobianco.
Co-corresponding authors: Caterina Gagliano and Marco Zeppieri.
Author contributions: Cappellani F and Capobianco M have made crucial and indispensable contributions towards the completion of the project as co-first authors; Cappellani F, Capobianco M, Visalli F, Khouyyi M, Musa M, Avitabile A, and Zeppieri M did the research and writing of the manuscript; Cappellani F, Capobianco M, Visalli F, Khouyyi M, Musa M, Avitabile A, Giglio R, Tognetto D, Gagliano C, D’Esposito F, and Zeppieri M wrote the outline, assisted in the writing of the draft and final paper; Cappellani F, Gagliano C, D’Esposito F, and Zeppieri M were responsible for the conception and design of the study; Cappellani F, Capobianco M, Visalli F, Khouyyi M, Musa M, Gagliano C, D’Esposito F, and Zeppieri M contributed to the scientific editing; Gagliano C and Zeppieri M have played important and indispensable roles in manuscript preparation as co-corresponding authors; Cappellani F, Capobianco M, Visalli F, Khouyyi M, Musa M, Avitabile A, Leandro I, Giglio R, Tognetto D, Gagliano C, D’Esposito F, and Zeppieri M assisted in the editing, making critical revisions of the manuscript and viewing all versions of the manuscript; all authors provided the final approval of the article.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Marco Zeppieri, MD, PhD, Consultant, Department of Ophthalmology, University Hospital of Udine, Maria Della Misericordia 15, Udine 33100, Italy. mark.zeppieri@asufc.sanita.fvg.it
Received: November 28, 2025
Revised: December 30, 2025
Accepted: February 10, 2026
Published online: April 15, 2026
Processing time: 137 Days and 21.9 Hours
Revised: December 30, 2025
Accepted: February 10, 2026
Published online: April 15, 2026
Processing time: 137 Days and 21.9 Hours
Core Tip
Core Tip: Artificial intelligence (AI) is transforming diabetic eye care through automated detection, risk assessment, and precision management. Deep learning algorithms utilized in fundus photography and optical coherence tomography imaging now equal or surpass experienced graders in the detection of diabetic retinopathy and macular edema. In addition to screening, AI incorporates multimodal data – such as systemic parameters, imaging biomarkers, and treatment histories – to predict disease progression and therapeutic outcomes. This review articulates contemporary clinical uses, technical underpinnings, and translational obstacles, while delineating potential trajectories for the development of explainable, morally robust, and globally scalable AI systems that can revolutionize the management of diabetic eye disease.