Diabetic retinopathy (DR) remains a significant cause of vision impairment and blindness, particularly in rural settings where access to specialized healthcare services is limited. The integration of artificial intelligence (AI) holds promise in revolutionizing the screening and diagnosis of DR, offering a scalable solution to bridge the gap in healthcare disparities. This systematic review synthesizes existing literature on AI applications tailored for screening and diagnosing diabetic retinopathy in rural areas. Through a comprehensive search across various databases, including PubMed, IEEE Xplore, and Google Scholar, a total of insert number studies meeting the inclusion criteria were identified. These studies encompassed a range of AI techniques, including deep learning algorithms, machine learning models, and image processing methods, deployed in diverse rural healthcare settings globally. The findings reveal that AI-based systems demonstrate high accuracy, sensitivity, and specificity in detecting diabetic retinopathy from fundus images, thereby enabling early identification and timely intervention. Moreover, the scalability and cost-effectiveness of these AI solutions make them particularly suitable for resource-constrained rural environments. However, several challenges persist, including the need for robust validation studies, integration with existing healthcare infrastructure, and addressing ethical and regulatory concerns. Additionally, considerations regarding data privacy, patient acceptance, and healthcare provider training are crucial for the successful implementation of AI-driven DR screening programs in rural settings. This systematic review underscores the transformative potential of AI technologies in improving access to diabetic retinopathy screening and diagnosis in rural areas. Future research should focus on addressing the identified challenges and optimizing AI systems to enhance their efficacy and accessibility in underserved communities.
Kuponiyi et al. (Wed,) studied this question.