A new Australian trial has demonstrated that an automated AI-powered camera can detect diabetic eye disease with over 93% accuracy in non-specialist medical settings. The study, led by Associate Professor Lisa Zhuoting Zhu and Sanil Joseph from the Center for Eye Research Australia and the University of Melbourne, along with Professor Mingguang He from the Hong Kong Polytechnic University, highlights the potential of AI to integrate into routine diabetes care. Diabetic eye disease affects millions globally, with over 529 million people living with diabetes and at risk of vision loss or blindness. Early detection and treatment can prevent blindness in up to 90% of cases, yet access to regular eye screenings remains a major challenge, especially in rural and underserved areas. The two-year trial, conducted between August 2021 and June 2023, involved more than 860 people with diabetes across general practitioner clinics, endocrinology clinics in Melbourne, and an Aboriginal Health Service in Western Australia. Participants used a portable retinal camera that captured images of their eyes while waiting for appointments. The images were analyzed by an AI algorithm trained on over 200,000 retinal scans graded by 21 ophthalmologists. Each participant received a printout with a QR code linking to their scan results, which they could bring to their next appointment. Those showing signs of diabetic eye disease were referred to eye specialists for further evaluation. Results were compared to assessments by human experts to verify accuracy. Both patients and healthcare providers completed satisfaction surveys. The study found the AI system achieved high accuracy in identifying diabetic eye disease, marking one of the first large-scale trials conducted in real-world clinical environments rather than controlled research settings. Researchers noted areas for improvement, such as refining detection in cases with mild or early-stage disease. However, the results suggest AI screening could significantly expand access to eye care, particularly in regions with limited availability of eye specialists. Dr. Zhu emphasized that AI scans could be especially beneficial in remote communities, where specialist services are scarce. She also highlighted the cost-efficiency of the approach, as it reduces the need for specialist involvement at every screening. Sanil Joseph added that the convenience of combining eye screening with existing medical appointments could improve patient adherence. Many people with diabetes face multiple health appointments and often delay or skip eye exams. Integrating screening into routine visits could help ensure timely detection. The findings, published in the British Journal of Ophthalmology, suggest that AI-powered eye screening has strong potential to become a standard part of diabetes management, helping prevent vision loss and easing pressure on healthcare systems.