{"ID":2881531,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.12506","arxiv_id":"2508.12506","title":"Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients","abstract":"Diabetic Retinopathy (DR) is a leading cause of vision loss in working-age individuals. Early detection of DR can reduce the risk of vision loss by up to 95%, but a shortage of retinologists and challenges in timely examination complicate detection. Artificial Intelligence (AI) models using retinal fundus photographs (RFPs) offer a promising solution. However, adoption in clinical settings is hindered by low-quality data and biases that may lead AI systems to learn unintended features. To address these challenges, we developed RAIS-DR, a Responsible AI System for DR screening that incorporates ethical principles across the AI lifecycle. RAIS-DR integrates efficient convolutional models for preprocessing, quality assessment, and three specialized DR classification models. We evaluated RAIS-DR against the FDA-approved EyeArt system on a local dataset of 1,046 patients, unseen by both systems. RAIS-DR demonstrated significant improvements, with F1 scores increasing by 5-12%, accuracy by 6-19%, and specificity by 10-20%. Additionally, fairness metrics such as Disparate Impact and Equal Opportunity Difference indicated equitable performance across demographic subgroups, underscoring RAIS-DR's potential to reduce healthcare disparities. These results highlight RAIS-DR as a robust and ethically aligned solution for DR screening in clinical settings. The code, weights of RAIS-DR are available at https://gitlab.com/inteligencia-gubernamental-jalisco/jalisco-retinopathy with RAIL.","short_abstract":"Diabetic Retinopathy (DR) is a leading cause of vision loss in working-age individuals. Early detection of DR can reduce the risk of vision loss by up to 95%, but a shortage of retinologists and challenges in timely examination complicate detection. Artificial Intelligence (AI) models using retinal fundus photographs (...","url_abs":"https://arxiv.org/abs/2508.12506","url_pdf":"https://arxiv.org/pdf/2508.12506v1","authors":"[\"E. Ulises Moya-Sánchez\",\"Abraham Sánchez-Perez\",\"Raúl Nanclares Da Veiga\",\"Alejandro Zarate-Macías\",\"Edgar Villareal\",\"Alejandro Sánchez-Montes\",\"Edtna Jauregui-Ulloa\",\"Héctor Moreno\",\"Ulises Cortés\"]","published":"2025-08-17T21:54:11Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","project_urls":"[\"https://gitlab.com/inteligencia-gubernamental-jalisco/jalisco-retinopathy\"]","has_code":false,"code_links":[{"ID":610818,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2881531,"paper_url":"https://arxiv.org/abs/2508.12506","paper_title":"Design and Validation of a Responsible Artificial Intelligence-based System for the Referral of Diabetic Retinopathy Patients","repo_url":"https://github.com/whatwg/fetch","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}