Evidence map›Paper›PMID 42620655›Full record

ArticleJournal of vitreoretinal diseases2026

Ability of Large Language Models to Answer Patients' Questions and Generate Educational Materials for Uncommon Retinal Conditions.

Samuel A Cohen, Prashant D Tailor, Adrian Au, Jennifer Vu, Alejandro I Marin, Pradeep S Prasad, Julie Kwon, Hamid Hosseini, Jayanth Sridhar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of vitreoretinal diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Samuel A CohenDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0002-8816-5015
Prashant D TailorDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Adrian AuDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Jennifer VuDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Alejandro I MarinDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Pradeep S PrasadDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Julie KwonDivision of Ophthalmology, Olive View-UCLA Medical Center, University of California Los Angeles, Sylmar, CA, USA.
Hamid HosseiniDepartment of Ophthalmology, UCLA Jules Stein Eye Institute, University of California Los Angeles, Los Angeles, CA, USA.
Jayanth SridharDivision of Ophthalmology, Olive View-UCLA Medical Center, University of California Los Angeles, Sylmar, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To assess the ability of large language models to accurately and comprehensively respond to frequently asked questions and generate patient education materials related to uncommon retinal conditions. Methods: A total of 50 frequently asked questions related to 10 uncommon retinal conditions were input into 3 large language models: ChatGPT-4o1, Google Gemini 2.0 Flash, and Microsoft Copilot (updated January 7, 2025). The accuracy and completeness of responses to frequently asked questions were evaluated by retina specialists using a Likert scale ranging from 1 (very inaccurate/not at all complete) to 5 (very accurate/completely complete), while readability was assessed using validated indices. The large language models were also instructed to generate patient education materials at specific reading levels, which were again evaluated for accuracy, completeness, and readability. Results: Responses to patient frequently asked questions were written at mean grade levels of 15.3 ± 1.4 (ChatGPT), 15.7 ± 1.9 (Gemini), and 14.6 ± 1.3 (Copilot), respectively ( Conclusions: Large language models can accurately respond to patients' frequently asked questions related to uncommon retinal conditions. Furthermore, large language models can effectively improve the readability of existing education materials for patients with varying levels of health literacy. A deeper understanding of large language model applications may facilitate their integration into clinical practice.

Indexed as

accuracyartificial intelligenceChatGPTCopilotGooglelarge language modelreadabilityretina

Identifiers

PMID42620655
PMCPMC13486393

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.