Effectiveness of a Large Language Model-Based Educational Tool on Intraocular Lens Options

Summary

Patients with cataracts disease need to choose what type of artificial lens will go into their eye prior to surgery date. Some lenses are standard and are usually covered by insurance. Other "premium" lenses have various benefits such as reducing the need for glasses but usually require out-of-pocket costs.

The combined busy outpatient clinic and complexity of artificial lens choices in the ever-changing world of cataract surgery tends to lead patients confused about their available lens options. There is an abundance of educational material present in premium lenses, however these are limited by accessibility and are standardized at single educational levels.

Therefore in the present study, we want to test whether giving patients a short LLM powered AI-guided explanation from Custom GPT from OpenAI of lens options prior to their consultation with their doctor can improve visit efficiency, physician explanation and patient understanding of lens options. We will compare two groups: standard of care versus standard of care plus AI education.

The LLM in this study is intended to provide supplemental information about premium intraocular lens(IOLs) options to study participants, and is no means supposed to replace a health care professional in the diagnosis, cure, treatment, and/or mitigation of disease. Study is analogous to giving a verified health pamphlet to a patient for them to view and learn different IOL options, in other words, facilitating patient understanding of their options.

The LLM will be trained by several health care professionals and MD specialists to provide sufficient instructions. Sources will include verified online resources and MD information.

The investigators hope to learn if a large language model-based educational tool can improve visit efficiency, physician explanation and patient understanding of intraocular lens options. New knowledge of this study could guide how cataract counseling is delivered in the future and may help clinics spend more time on individualized questions instead of repeating generic information.

Conditions

• Cataract and IOL Surgery
• Eye Disorders
• Cataract Extraction
• Cataract Surgery Experience
• Cataract Surgery
• Intraocular Lens

Interventions

OTHER

LLM-based Education

Participants will receive audio education powered by a large language model (LLM) before seeing the fellow or attending physician. 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 educational material about intraocular lens options and answer any questions the study participant has. This LLM-based education is for research purposes only. Afterward, participants will proceed to their scheduled visit.

Sponsors & Collaborators

• Stanford University

  lead OTHER

Principal Investigators

• Robert T Chang, MD · Stanford University

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-01-31

Primary Completion

2026-06-30

Completion

2026-06-30

Countries

• United States

Study Locations