ArticleCurrent opinion in ophthalmology2025
Ten years later: how is AI impacting retina care today?
Article in Current opinion in ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
purpose of reviewArtificial intelligence (AI) is transforming retina care, with deep learning (DL) models shaping a new era of improved screening accessibility, diagnostic precision, and personalized disease monitoring. This review highlights recent AI-powered clinical applications in diabetic retinopathy (DR), and age-related macular degeneration (AMD) care. RECENT
findingsSince the FDA's authorization of the first autonomous AI system for DR screening in 2018, multiple platforms have emerged, expanding access to diabetic eye care. Real-world studies have confirmed a significant improvement in screening adherence and diagnostic accuracy, illustrating AI's tangible impact on public health. Meanwhile, newly certified AI technologies that meet European regulatory standards are increasingly guiding clinical decision-making in the management of AMD and diabetic macular edema through automated analysis of optical coherence tomography (OCT) images. Most recently, FDA-authorized home OCT platforms are transforming AMD monitoring, enabling proactive and remote management of retinal fluid. SUMMARY: As AI increasingly empowers patients and providers, its widespread success still depends on ongoing work, including thorough validation, outcome-based metrics, and improved workflow integration. The next decade will reveal whether AI in retina care transitions from a promising innovation to an essential and indispensable tool in modern retina.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.