Write a PREreview

Screening for retinal disease is increasingly performed by general practitioners and other non‑ophthalmologist clinicians in primary care, especially where access to ophthalmology is limited and diagnostic accuracy may be suboptimal. To investigate the role of an automated fundus‑interpretation support solution in improving general physicians’ screening accuracy and referral decisions, we conducted a paired before–after study evaluating an AI‑based decision‑support tool. Four non-ophthalmologists who have been involved in screen fundus images in clinical practice reviewed 500 de‑identified color fundus photographs twice—first unaided and, after a washout period, with AI assistance. With AI support, diagnostic accuracy improved significantly from 82.8% to 91.1% (p < 0.0001), with the greatest benefit observed in glaucoma‑suspect and multi‑pathology cases. Clinicians retained final diagnostic authority, and a favorable safety profile was observed. These results demonstrate that AI assisted diagnosis aid can meaningfully augment non‑ophthalmologist screening and referral decision‑making in real‑world primary care, while underscoring the need for broader validation and implementation studies.