Advancing Diabetic Retinopathy Detection with AI and Deep Learning: Opportunities, Limitations, and Clinical Barriers

Abstract

Diabetic retinopathy (DR) remains one of the leading causes of preventable blindness globally, particularly among individuals with long-standing diabetes. Early detection through regular eye examinations is essential to prevent irreversible vision loss associated with advanced stages, such as proliferative diabetic retinopathy and diabetic macular oedema. Although screening programs have been successfully deployed in various healthcare systems, rising diabetes prevalence places a growing strain on medical infrastructure. As a result, there is a critical need for scalable, automated diagnostic tools. Recent advances in artificial intelligence, particularly deep learning using convolutional neural networks (CNNs), offer promising solutions for automated analysis of retinal images. These models have demonstrated high diagnostic performance in identifying DR stages and detecting macular oedema in imaging modalities like optical coherence tomography (OCT). Several AI algorithms have now received regulatory approval and are gradually being adopted in clinical workflows. Furthermore, innovations in portable imaging devices open new avenues for patient-led monitoring and remote diagnostics. However, despite their potential, current mobile imaging systems often fall short in achieving the resolution and consistency required for reliable DR detection when compared to standard fundus photography. Integration into telemedicine platforms could bridge this gap by enabling remote screening and centralized analysis, yet real-world implementation remains limited. Challenges such as legal regulations, software interoperability, and misalignment with existing national screening protocols continue to hinder widespread adoption. This paper explores the current state of AI-assisted diabetic retinopathy screening, evaluates the readiness of emerging technologies, and discusses key barriers that must be addressed to enable global deployment and improve patient outcomes.