The performance of ChatGPT-4 and Bing Chat in frequently asked questions about glaucoma

PurposeTo evaluate the appropriateness and readability of the responses generated by ChatGPT-4 and Bing Chat to frequently asked questions about glaucoma.MethodThirty-four questions were generated for this study. Each question was directed three times to a fresh ChatGPT-4 and Bing Chat interface. The obtained responses were categorised by two glaucoma specialists in terms of their appropriateness. Accuracy of the responses was evaluated using the Structure of the Observed Learning Outcome (SOLO) taxonomy. Readability of the responses was assessed using Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG), and Gunning- Fog Index (GFI).ResultsThe percentage of appropriate responses was 88.2% (30/34) and 79.2% (27/34) in ChatGPT-4 and Bing Chat, respectively. Both the ChatGPT-4 and Bing Chat interfaces provided at least one inappropriate response to 1 of the 34 questions. The SOLO test results for ChatGPT-3.5 and Bing Chat were 3.86 ± 0.41 and 3.70 ± 0.52, respectively. No statistically significant difference in performance was observed between both LLMs (<i>p</i> = 0.101). The mean count of words used when generating responses was 316.5 (± 85.1) and 61.6 (± 25.8) in ChatGPT-4 and Bing Chat, respectively (<i>p</i> < 0.05). According to FRE scores, the generated responses were suitable for only 4.5% and 33% of U.S. adults in ChatGPT-4 and Bing Chat, respectively (<i>p</i> < 0.05).ConclusionsChatGPT-4 and Bing Chat consistently provided appropriate responses to the questions. Both LLMs had low readability scores, but ChatGPT-4 provided more difficult responses in terms of readability.

Authors

Institutions

Publication Details

Journal
European Journal of Ophthalmology
Published
2025-02-19
DOI
https://doi.org/10.1177/11206721251321197
Citations
5
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
6.80
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The performance of ChatGPT-4 and Bing Chat in frequently asked questions about glaucoma

Levent Doğan, İ̇brahim Edhem Yılmaz
5 citations
European Journal of Ophthalmology
Retinal Imaging and Analysis
6.80
article

The performance of ChatGPT-4 and Bing Chat in frequently asked questions about glaucoma

Levent Doğan, İ̇brahim Edhem Yılmaz
article en
5 citations

Abstract

PurposeTo evaluate the appropriateness and readability of the responses generated by ChatGPT-4 and Bing Chat to frequently asked questions about glaucoma.MethodThirty-four questions were generated for this study. Each question was directed three times to a fresh ChatGPT-4 and Bing Chat interface. The obtained responses were categorised by two glaucoma specialists in terms of their appropriateness. Accuracy of the responses was evaluated using the Structure of the Observed Learning Outcome (SOLO) taxonomy. Readability of the responses was assessed using Flesch Reading Ease (FRE), Flesch Kincaid Grade Level (FKGL), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG), and Gunning- Fog Index (GFI).ResultsThe percentage of appropriate responses was 88.2% (30/34) and 79.2% (27/34) in ChatGPT-4 and Bing Chat, respectively. Both the ChatGPT-4 and Bing Chat interfaces provided at least one inappropriate response to 1 of the 34 questions. The SOLO test results for ChatGPT-3.5 and Bing Chat were 3.86 ± 0.41 and 3.70 ± 0.52, respectively. No statistically significant difference in performance was observed between both LLMs (<i>p</i> = 0.101). The mean count of words used when generating responses was 316.5 (± 85.1) and 61.6 (± 25.8) in ChatGPT-4 and Bing Chat, respectively (<i>p</i> < 0.05). According to FRE scores, the generated responses were suitable for only 4.5% and 33% of U.S. adults in ChatGPT-4 and Bing Chat, respectively (<i>p</i> < 0.05).ConclusionsChatGPT-4 and Bing Chat consistently provided appropriate responses to the questions. Both LLMs had low readability scores, but ChatGPT-4 provided more difficult responses in terms of readability.

European Journal of OphthalmologyVol. 35(4)
Kilis 7 Aralık University (TR)
Quality Education
Openalex Percentile: Top 4%
Retinal Imaging and Analysis
6.80
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The performance of ChatGPT-4 and Bing Chat in frequently asked questions about glaucoma — Levent Doğan, İ̇brahim Edhem Yılmaz · European Journal of Ophthalmology (2025) | TGRS Research Map | TGRS