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Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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Angle Closure Glaucoma: Treatment01:28

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Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...
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Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...
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Related Experiment Video

Updated: Sep 15, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment.

Mohammad Aminan1, S Solomon Darnell2, Mohammad Delsoz1

  • 1Department of Ophthalmology, University of Tennessee Health Sciences Center Memphis, Tennessee, United States.

Medrxiv : the Preprint Server for Health Sciences
|July 17, 2025
PubMed
Summary

A new glaucoma assessment tool, GlaucoRAG, uses a specialized retrieval-augmented generation (RAG) framework to improve diagnostic accuracy. This AI model shows promising results for glaucoma diagnosis and answering patient questions.

Keywords:
GlaucomaGlaucoma Specialized RAG (GlaucoRAG)Large Language Mdoel (LLM)Question Answering (QA)Retrieval-Augmented Generation (RAG)

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Accurate glaucoma assessment is complex due to the disease's chronic nature.
  • There is a need for evidence-based, accurate diagnostic models in glaucoma care.
  • Emerging technologies are being evaluated for their role in glaucoma assessment.

Purpose of the Study:

  • To evaluate the capabilities of GlaucoRAG, a glaucoma-specialized retrieval-augmented generation (RAG) framework.
  • To assess GlaucoRAG's effectiveness in diagnosing glaucoma and answering glaucoma-specific questions.
  • To compare GlaucoRAG's performance against established large language models (LLMs) and human specialists.

Main Methods:

  • Developed GlaucoRAG, a RAG framework using GPT-4.5-PREVIEW integrated with the R2R platform.
  • Created a comprehensive glaucoma knowledge base from over 1,800 articles, 15 guidelines, and 3 textbooks.
  • Tested diagnostic performance on 11 patient case reports and 250 multiple-choice questions (BCSC Self-Assessment), comparing outputs with glaucoma specialists and benchmark LLMs (GPT-4.5-PREVIEW, DeepSeek-R1).

Main Results:

  • GlaucoRAG achieved 81.8% accuracy on case reports, outperforming GPT-4.5-PREVIEW (72.7%) and DeepSeek-R1 (63.7%).
  • On BCSC questions, GlaucoRAG reached 91.2% accuracy, compared to 84.4% for GPT-4.5-PREVIEW and 76.0% for DeepSeek-R1.
  • RAGAS framework indicated 91% answer relevancy, 80% context recall, 70% faithfulness, and 59% context precision.

Conclusions:

  • The glaucoma-specialized LLM, GlaucoRAG, demonstrates encouraging diagnostic performance.
  • GlaucoRAG shows potential to complement glaucoma research and clinical practice.
  • The framework may enhance question-answering capabilities for glaucoma patients and clinicians.