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

Conditions

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

Eligibility Criteria

Sex

ALL

Age

18 Years - N/A

Healthy Volunteers

Not accepted

Interventions

• LLM-based Education — OTHER
  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.

Study Details

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.

Key Dates

Start date

Jan 31, 2026

Status verified

Dec 2025

Primary completion

Jun 30, 2026

Completion

Jun 30, 2026

Study Design

Enrollment

70 participants (estimated)

Allocation

RANDOMIZED

Intervention model

PARALLEL

Primary purpose

TREATMENT

Arms

• Experimental: LLM-based Education + Standard of Care
  * Before seeing the fellow, the participant will listen to a short, structured LLM powered AI-delivered educational session with Custom GPT (10 minutes or less). The intractable AI script explains standard monofocal IOLs and premium options (toric, extended depth of focus, multifocal, light adjustable lens), including benefits, trade-offs, and out-of-pocket costs. * The AI module may allow the patient to ask clarifying questions within scope of that script. This AI session is not currently part of standard care and is considered the experimental intervention. * The participant takes a patient satisfaction (CSQ-8) after their clinical visit with the fellow and attending
• No Intervention: Standard of Care
  * The participant skips the AI module and proceeds directly to routine fellow and attending counseling, which reflects current standard of care practice. * The participant takes a patient satisfaction (CSQ-8) after their clinical visit with the fellow and attending physician

Primary Outcome Measure

Total Consultation Time [ Time Frame: Same Day of Enrollment up to 2 hours ]

Locations (1)

Find similar trials in Palo Alto, CA

Related Studies

• Remimazolam for Cataract Surgery
  PHASE4 · Recruiting · University of California, Los Angeles · Los Angeles, California
• Effect of Vision Centers on Access to Eye Care and Eye Health Outcomes
  Not Yet Recruiting · University of California, San Francisco · San Francisco, California
• Effectiveness of a Large Language Model-Based Educational Tool on Visual Field Test Reliability in Glaucoma Patients
  Enrolling By Invitation · Stanford University · Palo Alto, California
• APPRAISE: Assessment of Pain and Posterior Synechiae Reduction With Atropine, an Investigation of Post-Surgical Eyes
  PHASE4 · Recruiting · University of California, San Francisco · San Francisco, California