Comprehensive Summary
This review explores the potential of artificial intelligence (AI) in ophthalmology, focusing on its ability to enhance workflows, improve patient outcomes, and reduce disparities. The robust performance of AI systems in diagnosing conditions such as diabetic retinopathy (DR), glaucoma, and age-related macular degeneration through advanced imaging techniques like fundus imaging and OCT is emphasized in this paper. For context, notable studies demonstrate high sensitivity and specificity in detecting DR, with systems like DeepDR achieving an AUC above 0.94. Yet, despite promising results, the paper emphasizes that very few AI systems have transitioned into clinical practice due to challenges in validation, generalizability, and integration into workflows.
Outcomes and Implications
AI holds significant promise for addressing gaps in eye care by enabling early detection and efficient screening in resource-constrained settings. For instance, automated DR detection can reduce blindness by identifying high-risk patients early. Moreover, the translation of AI into clinical settings requires overcoming barriers such as data annotation challenges, lack of generalizability, and insufficient integration with existing workflows. By addressing these obstacles, AI could theoretically become a cornerstone in ophthalmology, improving accessibility and outcomes at a global scale.