Evidence map›Paper›PMID 42830931›Full record

ReviewClinical ophthalmology (Auckland, N.Z.)2026

Large Language Model-Generated Patient Education Materials in Pediatric Ophthalmology: A Scoping Review.

Abdullahi Abdiaziz Mohamed, Thomas Scott/S Armstrong, Ojas Srivastava, Edsel Ing, Travis Pollock, Carlos Solarte

Abstract readReview
In one paragraph

Review in Clinical ophthalmology (Auckland, N.Z.), 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

6 authors.

Abdullahi Abdiaziz MohamedFaculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.ORCID 0009-0001-0646-045X
Thomas Scott/S ArmstrongFaculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
Ojas SrivastavaDepartment of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, AB, Canada.
Edsel IngDepartment of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, AB, Canada.
Travis PollockDepartment of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, AB, Canada.
Carlos SolarteDepartment of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0002-4763-5586

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective management of pediatric ophthalmic conditions such as myopia, amblyopia, and strabismus depend on caregivers understanding patient education materials (PEMs). However, many existing resources exceed recommended grade 8 reading levels. This review maps AI-generated PEMs in pediatric ophthalmology, summarizes reported outcomes across key educational domains, and identifies barriers and opportunities for clinical integration. Following PRISMA-ScR guidelines, a search of Ovid MEDLINE, Embase, and Scopus (January 2015-July 2025) identified studies evaluating LLM-generated PEMs in pediatric ophthalmology. Eligible studies evaluated artificial intelligence for pediatric ophthalmology patient education and reported at least one educational outcome. Data were extracted independently by two reviewers and synthesized descriptively. Twenty studies (2023-2025) evaluated 3286 AI-generated materials across 14 LLMs. Readability was assessed in 17 studies; 59% (37/63) of study-level LLM evaluations exceeded recommended 8th-grade reading levels. ChatGPT-4o evaluations were more often acceptable (52%) than ChatGPT-3.5 evaluations (18%), although these were not head-to-head comparisons. Median understandability (PEMAT-U) exceeded adequacy thresholds (≥70%). Professional society materials were more accurate and higher quality but significantly less readable. No included study explicitly classified outputs as hallucinations under its applied assessment framework; however, incomplete treatment-related content was common. Only two studies assessed patient or caregiver perspectives. LLM-generated PEMs in pediatric ophthalmology are generally understandable and accessible but often exceed readability standards and lack actionable guidance. While professional society materials remain more accurate, LLMs offer scalable tools for generating patient-centered resources. Integrating LLMs into clinician-supervised workflows and emphasizing co-designed, multimodal education strategies may enhance accessibility and clinical safety.

Indexed as

artificial intelligencehealth literacypatient educationreadability

Identifiers

PMID42830931
PMCPMC13634315

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.