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Purpose of Review <p>To examine the role of artificial intelligence (AI) in enhancing diabetic retinopathy (DR) screening among low-income immigrant populations in the United States, who are disproportionately affected by vision-threatening conditions, such as DR, glaucoma, cataracts, and refractive errors, and systemic barriers to healthcare access, including healthcare disparities, limited access to preventative care, and socioeconomic determinants of health.</p> Recent Findings <p>AI-based DR screening tools have demonstrated high sensitivity and specificity, offering scalable solutions that reduce provider burden and expand access in resource-limited settings. These technologies are particularly relevant to immigrant populations, who experience elevated DR prevalence. Studies also suggest AI can be effectively integrated into mobile platforms and non-traditional care models, though cost and infrastructure remain key challenges in underserved communities.</p> Summary <p>AI holds promise for advancing equitable DR screening, especially for historically marginalized groups. By addressing language barriers, affordability, and access constraints, AI-supported ophthalmic screening programs can help bridge healthcare gaps. Future research and policy should support culturally tailored, ethically sound implementation of AI to ensure sustained impact.</p>

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Artificial Intelligence in Ophthalmic Screening: Advancing Diabetic Retinopathy Detection in Low-Income Immigrant Populations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月5日

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  • Nicholas Joseph DeLuca,
  • Belen Wertheimer,
  • Zubair Ansari

摘要

Purpose of Review

To examine the role of artificial intelligence (AI) in enhancing diabetic retinopathy (DR) screening among low-income immigrant populations in the United States, who are disproportionately affected by vision-threatening conditions, such as DR, glaucoma, cataracts, and refractive errors, and systemic barriers to healthcare access, including healthcare disparities, limited access to preventative care, and socioeconomic determinants of health.

Recent Findings

AI-based DR screening tools have demonstrated high sensitivity and specificity, offering scalable solutions that reduce provider burden and expand access in resource-limited settings. These technologies are particularly relevant to immigrant populations, who experience elevated DR prevalence. Studies also suggest AI can be effectively integrated into mobile platforms and non-traditional care models, though cost and infrastructure remain key challenges in underserved communities.

Summary

AI holds promise for advancing equitable DR screening, especially for historically marginalized groups. By addressing language barriers, affordability, and access constraints, AI-supported ophthalmic screening programs can help bridge healthcare gaps. Future research and policy should support culturally tailored, ethically sound implementation of AI to ensure sustained impact.