Effectiveness of a Large Language Model-Based Educational Tool on Visual Field Test Reliability in Glaucoma Patients

Part of paid clinical trials in Palo Alto, California.

Sponsor
Stanford University
Study ID
NCT07327242
Status
Enrolling By Invitation

Conditions

  • Eye Disorders
  • Glaucoma
  • Visual Field Tests
  • Visual Fields

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • LLM-based Education — OTHER
    Participants will receive audiovisual education powered by a large language model (LLM) before their visual field test. The LLM will be presented using a 10 inch tablet or laptop device by a trained research team member. The interaction is intended to be self-guided, with no interference from the staff unless the LLM displays incorrect or "hallucinated" content. In such cases, the research staff will immediately correct any misinformation and record the occurrence, including details and frequency of the hallucination, for quality monitoring. The LLM module will deliver instructions, simulate the visual field test experience, and include a brief knowledge check. This LLM-based education is for research purposes only. Afterward, participants will proceed to their scheduled visual field test, which will include standard support from the clinic perimetrist.

Study Details

The purpose of this study is to evaluate whether a large language model (LLM)-based audiovisual educational tool improves the test time and reliability of standard automated perimetry (SAP) using the SITA Standard 24-2 protocol in English-speaking glaucoma patients. Glaucoma is a disease that can lead to blindness if not properly monitored and treated. One of the most important tests for glaucoma is the visual field (VF) test, which checks how well a person can see in different directions. However, this test is difficult for many patients to perform correctly, especially if they don't fully understand how it works. Unreliable test results can lead to repeated visits, wasted time, and incorrect treatment decisions. This study is testing whether a computer-based educational tool, powered by artificial intelligence (AI), can help patients better understand the VF test before taking it. The study team want to see if this helps make the test results more reliable. The goal is to improve the quality of care while reducing the burden on patients and clinic staff. The LLMs will be used as an educational tool only, not for the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease.

Key Dates

Start date
Jan 31, 2026
Status verified
Dec 2025
Primary completion
Jun 30, 2026
Completion
Jun 30, 2026

Study Design

Enrollment
80 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT

Arms

  • No Intervention: Standard of Care
    Patients will be informed of the standard protocol of Humphrey visual field testing by visual field technicians prior to their testing. Their education and knowledge will primarily come from the technicians themselves.
  • Experimental: LLM-based Education + Standard of Care
    Participants will receive audiovisual education powered by a large language model (LLM) before their visual field test, in addition to the standard of care information provided by the Humphrey visual field technicians.

Primary Outcome Measure

Humphrey Visual Field Test Taking Duration [ Time Frame: Same Day of Enrollment up to 2 hours ]

Locations (1)

FacilityCityStateZIPSite coordinators
Byers Eye InstitutePalo AltoCalifornia94303-

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