Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Glaucoma: Overview01:25

Glaucoma: Overview

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...
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

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...
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Extraction of Glaucoma Diagnosis, Type, and Severity from Clinical Notes using Secure Cloud-based Large Language Models.

medRxiv : the preprint server for health sciences·2026
Same author

GLLaucoMed: A Secure LLM-Powered Agentic Workflow for Automated Medication Extraction from Free-Text Glaucoma Clinical Notes.

medRxiv : the preprint server for health sciences·2026
Same author

Optic Disc Fundus Images Retain Biometric Identity Signals Under Deep Learning.

Research square·2026
Same author

Development and Pilot Testing of a Mobile App Psychosocial Intervention for Psychological Distress in Individuals with Glaucoma.

medRxiv : the preprint server for health sciences·2026
Same author

Reply to Comment on "RNFL Thickness in a Population-Based Cohort: The Canadian Longitudinal Study on Aging M2M (Machine-to-Machine) Study".

American journal of ophthalmology·2026
Same author

Reply to Comment on: Big Data or Big Bias? Interpreting Large-Scale Ophthalmic Studies.

American journal of ophthalmology·2026
Same journal

Pathogenicity Analysis of Two Novel CRB1 Mutations in Three Chinese Inherited Retinal Dystrophy Families and a Literature Review.

Translational vision science & technology·2026
Same journal

Gas-Lesion Contact and Postural Compliance After Vitrectomy With Tamponade: A Continuous Monitoring and 3D Quantitative Analysis.

Translational vision science & technology·2026
Same journal

Automated Deep Learning Quantification of Avascular Area and Intravitreal Neovascularization in Retinal Flatmounts of Rodent Oxygen-Induced Retinopathy Models.

Translational vision science & technology·2026
Same journal

The Effects of Myopia on Optic Disc Morphology and Retinal Vascular Geometry: A Study of Anisometropic Eyes.

Translational vision science & technology·2026
Same journal

Deep-ZOMA: A Deep Learning-Based Approach for Automated Morphometric Analysis of Zebrafish Larvae Ocular Structures.

Translational vision science & technology·2026
Same journal

Investigating the Correlation Between Choroidal Alteration and Visual Function Metrics in Dysthyroid Optic Neuropathy.

Translational vision science & technology·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

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

Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes Using a Local Large Language

Anshul Bhatnagar1, Rafael Scherer1, Gustavo A Samico1,2

  • 1Bascom Palmer Eye Institute, University of Miami, Miami, FL, USA.

Translational Vision Science & Technology
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) accurately identify medication non-adherence, visit non-adherence, and family history of glaucoma (FHoG) in clinical notes. This demonstrates LLMs

Related Experiment Videos

Last Updated: May 20, 2026

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

Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Electronic Health Records (EHRs) contain valuable clinical information but often lack structured data for research.
  • Identifying medication non-adherence, visit non-adherence, and family history of glaucoma (FHoG) is crucial for glaucoma management and research.
  • Traditional methods of extracting this data from EHRs can be time-consuming and prone to errors.

Purpose of the Study:

  • To evaluate the performance of a specialized medical large language model (LLM) in identifying medication non-adherence, visit non-adherence, and FHoG from clinical notes.
  • To compare the LLM's performance against structured EHR data for these specific clinical factors.

Main Methods:

  • Clinical notes from 1250 glaucoma-related encounters (2014-2024) were extracted from the Bascom Palmer Ophthalmic Repository.
  • Two glaucoma specialists manually labeled notes for non-adherence and FHoG to establish a reference standard.
  • A medical LLM (MedGemma-27B-text-it) was used to extract the same information, and its performance was quantified using accuracy, sensitivity, specificity, Jaccard index, and mean squared error (MSE).

Main Results:

  • The LLM achieved high accuracy in identifying medication non-adherence (0.91) and visit non-adherence (0.96).
  • For FHoG, the LLM demonstrated superior performance (accuracy 0.98, Jaccard index 0.99) compared to structured EHR fields (accuracy 0.49, Jaccard index 0.75).
  • The LLM significantly outperformed EHR fields in quantifying the number of relatives with glaucoma (MSE 0.05 vs. 0.85, P < 0.001).

Conclusions:

  • LLMs can effectively identify medication non-adherence, visit non-adherence, and FHoG from unstructured clinical notes with high accuracy.
  • Local LLM pipelines offer a scalable solution for researching glaucoma risk factors not readily available in discrete EHR fields.
  • This technology has the potential to enhance large-scale research and improve glaucoma patient care.