Evidence map›Paper›PMID 41316503›Full record

ArticleInternational journal of retina and vitreous2025

Evaluation of the artificial intelligence chatbots in frequently asked questions about retinitis pigmentosa: a comparative analysis between ChatGPT-4 and Gemini-2.0.

Özlem Biçer, Esra Şahlı

Abstract read
In one paragraph

Article in International journal of retina and vitreous, 2025. 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

2 authors.

Özlem BiçerDr Hulusi Alataş Elmadağ State Hospital, Ankara, Turkey. ozlembicer90@gmail.com.ORCID http://orcid.org/0000-0002-7638-4739
Esra ŞahlıDepartment of Ophthalmology, Ankara University School of Medicine, Ankara, Turkey.ORCID http://orcid.org/0000-0002-1355-7284

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo evaluate the accuracy and readability of answers to common retinitis pigmentosa (RP) questions from the popular generative artificial intelligence (AI) chatbots ChatGPT-4 and Gemini-2.0.

methodsIn March 2025, frequently asked questions about RP was entered to Google search tool, and the websites appearing on the first search page were selected for enrollment in the study. ChatGPT-4 and Gemini-2.0 were then prompted to generate responses about RP 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.' The AI chatbots' responses to 30 questions about RP, frequently asked by patients, were evaluated by two ophthalmologists using a five-point Likert scale, with scores ranging from 1-5. Additionally, 8 readability indices, including Average Reading Level Consensus Calculator (ARLC), Automated Readability Index (ARI), Flesch Reading Ease (FRE), Gunning Fog Index (GFOG), Flesch-Kincaid Grade Level (FKGL), Coleman-Liau Index (CL), Simple Measure of Gobbledygook (SMOG), and Forcast Readability Formula (FRF) were calculated using an online calculator, Readabilityformulas.com, to assess the ease of comprehension of each answer.

resultsNo significant difference showed in accuracy both standard and simplified AI chatbot responses (p = 0.557, p = 0.090). In particular, almost all readability indices suggest that standard AI chatbot responses require a higher level of education for comprehension, whereas simplified responses require a lower level of education. Although Gemini-2.0 standard responses were more readable than ChatGPT-4 standard responses according to ARI, GFOG and FRF scores (p = 0.014, p = 0.040, and p = 0.001), Gemini-2.0 simplified responses were more readable than ChatGPT-4 simplified responses solely according to FRF scores (p = 0.016).

conclusionsThis study shows that ChatGPT-4 and Gemini-2.0 can provide patients with an avenue to access comprehensive and accurate information about, tailored RP to their educational level.

Indexed as

Artificial intelligenceChatGPT-4Gemini-2.0ReadabilityRetinitis pigmentosa

Identifiers

PMID41316503
PMCPMC12763912

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.