A Review of Machine Learning Approaches to Eye Disease Detection: Current Trends, Challenges and Future Directions

Background/Objectives: Vision impairment and blindness remain a major global health challenge, with glaucoma, diabetic retinopathy, age-related macular degeneration, cataract, corneal disease, and retinopathy of prematurity representing important causes of preventable or treatable vision loss when detected early. This clinical need has accelerated research on machine learning approaches for automatic detection of eye diseases, grading, and segmentation. However, existing reviews often focus on a single disease or method. They also give limited attention to their suitability and applicability for reliable clinical deployments. This review synthesizes traditional machine learning, deep learning, vision transformers, ensemble and hybrid models, segmentation methods, and explainable AI approaches for eye disease detection. It further examines task formulation, dataset characteristics, evaluation practices, and deployment constraints, with emphasis on clinical readiness rather than benchmark performance alone. Methods: A structured narrative review was conducted using recently published articles from reputable databases and repositories, including IEEE Xplore, PubMed/MEDLINE, ScienceDirect, Scopus and arXiv. The review critically analyzes the progression from traditional machine learning to advanced models, including deep learning, vision transformers, ensemble models and explainable artificial intelligence. Studies were critically analyzed according to task formulations, dataset characteristics, segmentation approaches, evaluation metrics, explainability, and deployment constraints. Results: The synthesis shows that class imbalance, limited demographic and geographic diversity in available datasets, weak external validation, and scarce prospective evidence continue to separate benchmark performance from clinical readiness. Explainability is also often evaluated through visual plausibility rather than validated faithfulness. Currently, data-driven diabetic retinopathy screening is closest to clinical readiness, particularly for distinguishing referable from non-referable cases, whereas multi-label detection, rare disease detection, and cross-device deployment remain at the research stage. Conclusions: Progress towards clinical adoption requires stronger alignment between experimental evaluation and real-world deployment. This includes external validation and the development of equitable, interpretable, and resource-aware systems.