Using AI to create easy-to-understand summaries of eye care notes
Official title: Evaluating AI-Generated Plain Language Summaries on Patient Comprehension of Ophthalmology Notes Among English-Speaking Patients
Run by University of California, Los Angeles · NCT06859216 · Data updated 2026-11-12
This study is testing if using AI to create simpler summaries of eye care notes can help patients understand their diagnosis and treatment better.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 460 (estimated) |
| Sponsor | University of California, Los Angeles Academic / other |
| Locations | 1 site (Los Angeles, California) |
| Status | Recruiting |
| Started | March 1, 2025 |
| Main results expected | April 30, 2026 |
| Study ends | June 1, 2026 |
| Registry record updated | March 5, 2026 |
| Trial ID | NCT06859216 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are English-speaking adults aged 18 and older who are receiving ophthalmology care at the Jules Stein Eye Institute.
✕Probably not a fit
Patients with known cognitive impairments or those unable to provide informed consent may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This clinical trial evaluates whether plain language summaries generated by artificial intelligence can enhance patient understanding of ophthalmology notes. Adults receiving eye care at the Jules Stein Eye Institute will be randomly assigned to receive either standard medical notes or notes supplemented with AI-generated summaries that simplify complex information. Participants will complete a survey to assess their comprehension of their diagnosis and treatment, as well as their confidence in their care. The study aims to improve communication between patients and healthcare providers through clearer information.
Why it matters
Potential benefit: If successful, this approach could significantly enhance patient comprehension and confidence in their eye care.
How similar studies have gone: Other studies have shown promise in using AI for patient education, but this specific approach is relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age ≥ 18 years English-speaking Receiving ophthalmology care at the Jules Stein Eye Institute Able to provide informed consent Exclusion Criteria: * Known cognitive impairments (e.g., dementia, intellectual disability) that would affect comprehension Prisoners or wards of the state Unable to provide informed consent
Where it's running
-
UCLA
Los Angeles, California, United States Recruiting
Study contacts
- Principal investigator: Prashant Tailor, MD — University of California, Los Angeles
- Study coordinator: Prashant Tailor, MD
- Email: ptailor@mednet.ucla.edu
- Phone: 4043166920
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
AI-assisted measurement of gummy smiles from photos
A computer program using artificial intelligence will try to identify and measure gummy smiles from patient photos to see if its measurements match those of expert dentists.
-
Benchmarking AI language models against tumor boards for oncology treatment decisions
This project tests whether frontier AI language models can match the treatment recommendations of multidisciplinary tumor boards for common cancers using standardized synthetic cases.
-
AI-assisted gender prediction from the jawbone using CBCT measurements
A retrospective study tests whether AI can predict a person’s gender from measurements of the mandible taken from existing CBCT scans.
-
Large language models for turning structured dental radiology data into narrative reports
This study tests whether a language model can convert organized dental X‑ray findings into clear, complete radiology reports.
-
Smartphone-based AI eye disease screening for anterior segment conditions
A smartphone-powered AI system tests whether it can identify 16 eye conditions from images captured on a phone, without needing internet access, in adults.
-
Detecting freezing of gait at home using AI and wearable sensors
This project tests whether an AI algorithm using wearable sensors can accurately detect freezing of gait in people with Parkinson's disease while they go about daily life at home.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.