ArticleJournal of medical Internet research2024
Large Language Models May Help Patients Understand Peer-Reviewed Scientific Articles About Ophthalmology: Development and Usability Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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.
Who cites it
14 citing papers in PubMed.
- Large Language Model Simplification of Open Access Pediatric Strabismus Literature: Cross-Sectional Validation of Readability and Clinical Fidelity.JMIR formative research · 2026Article
- Information leaflets vs artificial intelligence: comparing perceptions of stroke survivors and professionals in a mixed-methods study.European stroke journal · 2026Article
- Improving the Understandability of Clinical Guidelines: Development and Evaluation of a GPT-4-Based Pipeline.Journal of medical Internet research · 2026Article
- Assessing the Quality of AI Responses to Patient Concerns About Axial Spondyloarthritis: Delphi-Based Evaluation.JMIR AI · 2026Article
- Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background.Frontiers in medicine · 2026Article
- Large Language Model-Generated Patient Education Materials in Pediatric Ophthalmology: A Scoping Review.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- Evaluating multimodal large language models for differential diagnosis of high myopia versus high myopia with Glaucoma.Frontiers in medicine · 2026Article
- Automated Multitier Tagging of Chinese Online Health Education Resources Using a Large Language Model: Development and Validation Study.Journal of medical Internet research · 2025Article
- Feeding intelligence: comparative evaluation of ChatGPT and clinical guidelines for nutritional management in head and neck cancer.Journal of translational medicine · 2025Article
- Using Artificial Intelligence ChatGPT to Access Medical Information About Chemical Eye Injuries: Comparative Study.JMIR formative research · 2025Article
- Application of Large Language Models in Stroke Rehabilitation Health Education: 2-Phase Study.Journal of medical Internet research · 2025Article
- Artificial intelligence in patient education: evaluating large language models for understanding rheumatology literature.Frontiers in digital health · 2025Article
- Advances in the application of artificial intelligence in ophthalmic education and clinical training.Frontiers in medicine · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundAdequate health literacy has been shown to be important for the general health of a population. To address this, it is recommended that patient-targeted medical information is written at a sixth-grade reading level. To make well-informed decisions about their health, patients may want to interact directly with peer-reviewed open access scientific articles. However, studies have shown that such text is often written with highly complex language above the levels that can be comprehended by the general population. Previously, we have published on the use of large language models (LLMs) in easing the readability of patient-targeted health information on the internet. In this study, we continue to explore the advantages of LLMs in patient education.
objectiveThis study aimed to explore the use of LLMs, specifically ChatGPT (OpenAI), to enhance the readability of peer-reviewed scientific articles in the field of ophthalmology.
methodsA total of 12 open access, peer-reviewed papers published by the senior authors of this study (ET and RA) were selected. Readability was assessed using the Flesch-Kincaid Grade Level and Simple Measure of Gobbledygook tests. ChatGPT 4.0 was asked "I will give you the text of a peer-reviewed scientific paper. Considering that the recommended readability of the text is 6th grade, can you simplify the following text so that a layperson reading this text can fully comprehend it? - Insert Manuscript Text -". Appropriateness was evaluated by the 2 uveitis-trained ophthalmologists. Statistical analysis was performed in Microsoft Excel.
resultsChatGPT significantly lowered the readability and length of the selected papers from 15th to 7th grade (P<.001) while generating responses that were deemed appropriate by expert ophthalmologists.
conclusionsLLMs show promise in improving health literacy by enhancing the accessibility of peer-reviewed scientific articles and allowing the general population to interact directly with medical literature.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.