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

1.2K
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...
1.2K
Open Angle Glaucoma: Treatment01:27

Open Angle Glaucoma: Treatment

953
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...
953
Angle Closure Glaucoma: Treatment01:28

Angle Closure Glaucoma: Treatment

1.1K
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...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Sparse-Observation Multi-Horizon Glaucoma Progression Forecasting with Biologically Constrained Temporal Consistency: A Glaucoma Case Study.

Research square·2026
Same author

Cracking the Code: Which Ocular Symptoms Predict Dry Eye Signs? Insights From a Large International Sicca Registry.

Arthritis care & research·2026
Same author

Alumni in Focus: Profile, scientific production, and career of graduates from the postgraduate program in ophthalmology at UNIFESP.

Arquivos brasileiros de oftalmologia·2026
Same author

Performance of generative large language models in answering questions from the Brazilian Retina and Vitreous Society certification exam.

Arquivos brasileiros de oftalmologia·2026
Same author

Detecting glaucoma progression through optic nerve head hemoglobin concentration using automated colorimetric analysis.

European journal of ophthalmology·2026
Same author

Chasing shadows: case series of six posterior segment manifestations of ocular tuberculosis.

AME case reports·2026

Related Experiment Video

Updated: Jan 13, 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

1.0K

Performance of a Small Language Model Versus a Large Language Model in Answering Glaucoma Frequently Asked Patient

Adriano Cypriano Faneli1,2, Rafael Scherer1, Rohit Muralidhar1

  • 1Bascom Palmer Eye Institute, University of Miami, 900 NW 17th St, Miami, FL, 33136, United States, 1 305-326-6000.

JMIR AI
|January 6, 2026
PubMed
Summary
This summary is machine-generated.

A specialized small language model (SLM) performed comparably to a large language model (LLM) in answering glaucoma questions, though both models produced content too complex for patients.

Keywords:
ChatGPT4.0glaucomalarge language modelonline health informationsmall language model

More Related Videos

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.8K
Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

550

Related Experiment Videos

Last Updated: Jan 13, 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

1.0K
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

6.8K
Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

550

Area of Science:

  • Ophthalmology and Artificial Intelligence
  • Natural Language Processing in Healthcare

Background:

  • Large language models (LLMs) show potential in answering patient questions in ophthalmology.
  • Concerns exist regarding LLM use due to privacy and patient safety risks from inaccuracies.

Purpose of the Study:

  • To compare the performance of a locally trained small language model (SLM) with a large language model (LLM) in answering patient questions about glaucoma.
  • To evaluate the accuracy and readability of answers generated by both models.

Main Methods:

  • 35 frequently asked glaucoma questions across 6 domains were compiled.
  • A SLM (retrieval-augmented generation) and ChatGPT 4.0 (LLM) answered the questions.
  • Three glaucoma specialists rated answer accuracy, and readability was assessed using standard formulas.

Main Results:

  • The SLM (mean score 7.9) and LLM (mean score 7.4) showed comparable quality scores (out of 9).
  • Accuracy was consistent across all glaucoma domains for both models.
  • Both models generated answers that were too complex for the average layperson.

Conclusions:

  • While accurate, both LLM and SLM answers were difficult for patients to understand.
  • The specialized SLM offers a viable, customizable, and cost-effective alternative for clinical NLP applications in ophthalmology.