This Diabetes Care commentary questions if deep learning (DL) -enabled diabetic retinopathy (DR) screening from fundus photos is a true techno-clinical game-changer amid global diabetes burdens. Responding to Ran et al.'s review of 47 validation studies showing ~95% accuracy but gaps in low/middle-income countries (LMICs), it stresses needs for transparent methods, economic proof, and equitable integration to handle 360 million annual screens. It urges rigorous data curation, full reporting per TRIPOD-AI standards , and context-aware cost-effectiveness for high-income (labor-costly) vs. LMIC (tech-limited) settings to shift from hype to real vision-saving impact. Dive into the article, equip your practice with AI that truly sees!
Click here to gain insights. ##Reference## Dave D, Steinhubl SR, McQueen RB. Deep Learning–Enabled Diabetic Retinopathy Screening: A Techno-Clinical…
