ReviewOphthalmology science2026
The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.
Review in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
37 authors.
Funding
Abstract
Clinical Relevance: The retina is a unique and accessible window to the central nervous system and its vasculature. Ocular fundus examination is crucial for identifying diagnostic red flags indicative of life- and sight-threatening conditions, such as papilledema and central retinal artery occlusion. In the emergency department (ED), prompt recognition of these conditions is essential to reduce the risk of vision loss and serious complications. Methods: This review summarizes advances in ocular fundus examination, imaging, and artificial intelligence (AI) for emergency care. Results: The direct ophthalmoscope, a traditional tool for ocular fundus examination by nonophthalmology providers in the ED, is rarely utilized due to technical limitations and lack of user skill and confidence. Recent advancements in retinal imaging technologies have introduced ocular fundus cameras with OCT as valuable alternatives. The advent of nonmydriatic and portable imaging devices has significantly expanded accessibility, enabling remote image interpretation and facilitating teleophthalmology consultations. Moreover, AI, especially deep-learning technology, has demonstrated considerable potential for automated ocular image interpretation. The latest developments in large language models show promise in providing aids in diagnosis and management. Conclusions: Further research is needed to validate the reliability of AI-powered ocular fundus assessment and to explore how these emerging technologies can be effectively integrated into ED practice to enhance ocular fundus examination and improve patient care.
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Registered trials
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