Google Lens: A potential cost-effective screening tool for diabetic retinopathy

Abstract

Background: Diabetic retinopathy (DR) is a major, sight-threatening complication of diabetes mellitus. Blindness from DR can be prevented by successful and proactive screening. However, DR is screened in less than half of the patients because of barriers in availability, affordability, accessibility, and awareness. Although artificial intelligence (AI)-based algorithms are being evaluated for DR screening, they have limitations of infrastructure, accessibility, training, and manpower cost. Therefore, simpler and more practical DR screening tools should be explored.
 Hypothesis: Google Lens, an easily available, vision- and AI-based application in most smartphones, is a potential tool for cost-effective DR screening. It recognises images through a visual analysis based on neural networking. Thus, it can recognize retinal disorders, such as DR, in images. The development and adoption of Google Lens-based DR screening would have several advantages over the conventional hospital/specialist/healthcare facility-based approach, including widespread accessibility, acceptable accuracy, reduction in the direct cost of healthcare for patients with diabetes mellitus, and active patient participation in self-care.
 Conclusions: DR screening, detection, and grading using Google Lens is a feasible and effective option. Despite current limitations, it could transform DR screening from a costly, hospital- and expert-based method to a cost-effective, self-applicable, and home-based one. However, diagnostic accuracy studies comparing the index test with Google Lens-based screening are required to determine the usability and validity of this proposed screening tool for DR.