Evidence map›Paper›PMID 39435137›Full record

ArticleOphthalmology science

Evaluation of the Appropriateness and Readability of ChatGPT-4 Responses to Patient Queries on Uveitis.

S Saeed Mohammadi, Anadi Khatri, Tanya Jain, Zheng Xian Thng, Woong-Sun Yoo, Negin Yavari, Vahid Bazojoo, Azadeh Mobasserian, Amir Akhavanrezayat, Ngoc Trong Tuong Than and 8 more

Abstract read
In one paragraph

Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. Reply.The journal of allergy and clinical immunology. Global · 2025
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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

18 authors.

S Saeed MohammadiDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Anadi KhatriDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Tanya JainDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Zheng Xian ThngDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Woong-Sun YooDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Negin YavariDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Vahid BazojooDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Azadeh MobasserianDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Amir AkhavanrezayatDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Ngoc Trong Tuong ThanDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Osama ElarabyDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Battuya GanboldDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Dalia El FekyDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Ba Trung NguyenDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Cigdem YasarDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Ankur GuptaDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Jia-Horung HungDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.
Quan Dong NguyenDepartment of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To compare the utility of ChatGPT-4 as an online uveitis patient education resource with existing patient education websites. Design: Evaluation of technology. Participants: Not applicable. Methods: The term "uveitis" was entered into the Google search engine, and the first 8 nonsponsored websites were selected to be enrolled in the study. Information regarding uveitis for patients was extracted from Healthline, Mayo Clinic, WebMD, National Eye Institute, Ocular Uveitis and Immunology Foundation, American Academy of Ophthalmology, Cleveland Clinic, and National Health Service websites. ChatGPT-4 was then prompted to generate responses about uveitis in both standard and simplified formats. To generate the simplified response, the following request was added to the prompt: 'Please provide a response suitable for the average American adult, at a sixth-grade comprehension level.' Three dual fellowship-trained specialists, all masked to the sources, graded the appropriateness of the contents (extracted from the existing websites) and responses (generated responses by ChatGPT-4) in terms of personal preference, comprehensiveness, and accuracy. Additionally, 5 readability indices, including Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, and Simple Measure of Gobbledygook index were calculated using an online calculator, Readable.com, to assess the ease of comprehension of each answer. Main Outcome Measures: Personal preference, accuracy, comprehensiveness, and readability of contents and responses about uveitis. Results: A total of 497 contents and responses, including 71 contents from existing websites, 213 standard responses, and 213 simplified responses from ChatGPT-4 were recorded and graded. Standard ChatGPT-4 responses were preferred and perceived to be more comprehensive by dually trained (uveitis and retina) specialist ophthalmologists while maintaining similar accuracy level compared with existing websites. Moreover, simplified ChatGPT-4 responses matched almost all existing websites in terms of personal preference, accuracy, and comprehensiveness. Notably, almost all readability indices suggested that standard ChatGPT-4 responses demand a higher educational level for comprehension, whereas simplified responses required lower level of education compared with the existing websites. Conclusions: This study shows that ChatGPT can provide patients with an avenue to access comprehensive and accurate information about uveitis, tailored to their educational level. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Indexed as

Artificial intelligenceChatGPTPatient educationUveitis

Identifiers

PMID39435137
PMCPMC11492124

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.