Artificial Intelligence in Eye Disease: A Systematic Review of Screening, Diagnosis, Prognosis, Treatment-Response Prediction, and Clinical Decision Support
5 affiliations
- 1Department of Optometry, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
- 2Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
- 3Research Department, Prince Sultan Cardiac Center, Ministry of Defense Health Services, Riyadh 12426, Saudi Arabia
- 4Research and Innovation Institute, Ministry of Defense Health Services, Riyadh 12426, Saudi Arabia
- 5College of Medicine, King Saud University, Riyadh 11472, Saudi Arabia
Abstract
Background/Objectives: Artificial intelligence (AI) is expanding eye care beyond image-based detection toward prognosis, treatment-response prediction, monitoring, triage, and clinician-facing decision support. We evaluated AI across eye-disease screening, diagnosis, prognosis, treatment response, and decision support, emphasizing validation, explainability, and clinical readiness. Methods: This PROSPERO-registered review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Six databases plus supplementary routes identified English-language literature published from 1 January 2015 through 10 August 2026. Included studies underwent design-mapped Joanna Briggs Institute (JBI)/adapted-JBI item-level appraisal; heterogeneity precluded meta-analysis. Results: The evidence base comprised 270 studies. Diabetic retinopathy/diabetic macular oedema was the largest clinical area (82), followed by retinal/multi-disease AI (52), glaucoma (46), and age-related macular degeneration (AMD)/macular disease (29); convolutional neural network (CNN)/convolutional deep learning was the largest primary method group (136). External validation was confirmed in 130 studies, including 95 with a clinical/geographic/prospective/real-world external-validation component (86 clinical-only and nine mixed clinical + technical); 49 had a strict direct clinical comparator and 90 used explainability. Pediatric/pediatric-relevant evidence accounted for 30 studies, while explicit optometry/optometrist involvement appeared in only three. Conclusions: Ophthalmic AI spans diagnosis through clinical management, but translation remains limited by heterogeneous outcomes, incomplete independent validation, few direct comparators, and sparse evaluation of safety, workflow effects, and patient outcomes.
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BibTeX
@article{alharbi2026artificial,
title = {{Artificial Intelligence in Eye Disease: A Systematic Review of Screening, Diagnosis, Prognosis, Treatment-Response Prediction, and Clinical Decision Support}},
author = {Manal M. Alharbi and Emtenan M. Alharbi and Alanoud Suliman Alsalamah and Hanan Asiri and Najla Beati and Renad AlAtawi and Amr Arafat},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207957},
url = {https://www.mdpi.com/2077-0383/15/20/7957},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Eye Disease: A Systematic Review of Screening, Diagnosis, Prognosis, Treatment-Response Prediction, and Clinical Decision Support
AU - Manal M. Alharbi
AU - Emtenan M. Alharbi
AU - Alanoud Suliman Alsalamah
AU - Hanan Asiri
AU - Najla Beati
AU - Renad AlAtawi
AU - Amr Arafat
PY - 2026
DA - 2026/10/01
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207957
UR - https://www.mdpi.com/2077-0383/15/20/7957
AB - Background/Objectives: Artificial intelligence (AI) is expanding eye care beyond image-based detection toward prognosis, treatment-response prediction, monitoring, triage, and clinician-facing decision support. We evaluated AI across eye-disease screening, diagnosis, prognosis, treatment response, and decision support, emphasizing validation, explainability, and clinical readiness. Methods: This PROSPERO-registered review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Six databases plus supplementary routes identified English-language literature published from 1 January 2015 through 10 August 2026. Included studies underwent design-mapped Joanna Briggs Institute (JBI)/adapted-JBI item-level appraisal; heterogeneity precluded meta-analysis. Results: The evidence base comprised 270 studies. Diabetic retinopathy/diabetic macular oedema was the largest clinical area (82), followed by retinal/multi-disease AI (52), glaucoma (46), and age-related macular degeneration (AMD)/macular disease (29); convolutional neural network (CNN)/convolutional deep learning was the largest primary method group (136). External validation was confirmed in 130 studies, including 95 with a clinical/geographic/prospective/real-world external-validation component (86 clinical-only and nine mixed clinical + technical); 49 had a strict direct clinical comparator and 90 used explainability. Pediatric/pediatric-relevant evidence accounted for 30 studies, while explicit optometry/optometrist involvement appeared in only three. Conclusions: Ophthalmic AI spans diagnosis through clinical management, but translation remains limited by heterogeneous outcomes, incomplete independent validation, few direct comparators, and sparse evaluation of safety, workflow effects, and patient outcomes.
SN - 2077-0383
ER -