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

NCT07317661 | Enrolling By Invitation

• 1 other identifier: 82807
• Study Type: interventional
• Target: 70
• Locations: 1 country
• Sites: 1

Brief 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

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

Keywords

LLM; AI; GPT; ChatGPT; Custom GPT; CustomGPT; education; LLM-based education

Outcome Measures

Primary Outcomes (1)

• Total Consultation Time

  Total consultation time of both fellow and attending physician in their visit with the study participant will be recorded in minutes.

  Same Day of Enrollment up to 2 hours

Secondary Outcomes (2)

• Patient satisfaction scale score as measured by Client Satisfaction Questionnaire-8 (CSQ-8)

  Same Day of Enrollment up to 2 hours

• Percentage of Monofocal Lens Chosen as IOL of Choice Between Arms

  Up to 4 weeks post enrollment

Study Arms (2)

LLM-based Education + Standard of Care

EXPERIMENTAL

* 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

Other: LLM-based Education

Standard of Care

NO INTERVENTION

* 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

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.

LLM-based Education + Standard of Care

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Age 18 or older
• Presenting for cataract evaluation or preoperative cataract counseling in the ophthalmology clinic
• Able to provide informed consent
• English-speaking
• No prior cataract surgery in either eye (so that all patients are making a first-eye IOL decision)

You may not qualify if:

• Any cognitive impairment or hearing impairment that prevents meaningful counseling or survey completion
• Urgent ocular condition requiring immediate attention that would override routine cataract counseling (for example, acute retinal detachment)
• Patient declines or is unable to complete the brief post-visit survey
• Has ocular conditions that would impact eligibility of non-monofocal lens options

Sponsors & Collaborators

• Stanford University: lead

Study Sites (1)

Byers Eye Institute

Palo Alto, California, 94303, United States

Location

MeSH Terms

Conditions

Eye Diseases; Cataract

Condition Hierarchy (Ancestors)

Lens Diseases

Study Officials

• Robert T Chang, MD

  Stanford University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: CARE PROVIDER
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Ophthalmology

Study Record Dates

• First Submitted: December 19, 2025
• First Posted: January 5, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: January 5, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)