Data and code from: Evaluating an AI-assisted triage workflow for retinal diseases

Le résumé fourni par la source

The clinical value of artificial intelligence (AI) in fundus photography depends on its ability to improve workflow efficiency as well as diagnostic performance. We evaluated an AI-assisted negative-screening workflow for diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) using 6,904 color fundus photographs independently graded by three retina specialists. In this workflow, AI-negative images were classified as negative without ophthalmologist review, whereas AI-positive images were referred for human interpretation. For each reader–disease pair, the reference standard was defined by agreement between the other two readers, and discordant cases were excluded. The workflow substantially reduced direct ophthalmologist review to 7.3%–8.0% of images for DR, 11.7%–11.9% for RVO, and 13.5%–16.8% for AMD. Our findings indicate that AI-assisted negative screening can substantially reduce ophthalmologist workload across multiple retinal diseases, but with disease-specific safety trade-offs. These results support a disease-specific implementation strategy that carefully balances workload reduction against missed positive cases.

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