Bridging the Vision Gap: Role of AI in Diabetic Retinopathy Detection and Clinical Feasibility in Low Resource Settings

Diabetic retinopathy is the leading cause of vision loss worldwide, especially in low-resource settings and underserved or rural communities where routine screening is not widely available. Early detection prevents irreversible vision loss, but traditional screening methods require specialized personnel and equipment that are not universally available. These gaps can potentially be filled by artificial intelligence which allows early accurate and flexible detection of diabetic retinopathy. Deep learning models like convolutional neural networks and AI algorithms show similar sensitivity and specificity as human graders in diabetic retinopathy detection. Tools like IDx-DR and EyeArt which have been approved by authorities and adequate regulatory bodies, show promising results for adaptable screening. Integration with mobile and telemedicine platforms, in addition to innovations that improve data diversity and algorithm transparency will increase accessibility, accuracy and trust in artificial intelligence. This review takes a look at artificial intelligence in ophthalmology focusing on its use in diabetic retinopathy detection and also discusses implementation problems it faces like ethics issues and algorithmic bias, data quality and representation, data privacy concerns and limited digital literacy and sustainability in low resource settings. With all these barriers aside, integrating artificial intelligence with telemedicine platforms, mobile diagnostics, national health systems, and recommending policy and collaborations for artificial intelligence deployment can close the gap between diabetic retinopathy screening and eye care access in low resource settings. This paper concludes by recommending more investment, more teamwork across various professions and more inclusive policies to ensure artificial intelligence driven diabetic retinopathy screening technologies benefit all people particularly those most at risk of preventable blindness.