ArticleTurkish journal of ophthalmology2024
Readability and Appropriateness of Responses Generated by ChatGPT 3.5, ChatGPT 4.0, Gemini, and Microsoft Copilot for FAQs in Refractive Surgery.
Article in Turkish journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- When Large Language Models (LLMs) walk into a Bachelor's in optometry examination: Comparing the performances of LLMs and bachelor of optometry students.Indian journal of ophthalmology · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Evolving Minds: Natural Learning vs. Artificial Learning in Ophthalmology Training.Turkish journal of ophthalmology · 2026Observational
- Can large language models provide quality, reliable and readable information about sudden infant death syndrome?BMC pediatrics · 2026Article
- Artificial intelligence as a source of psychotherapy-related psychoeducation: a cross-sectional content analysis of information on eye movement desensitization and reprocessing.Frontiers in psychiatry · 2026Article
- Bilingual evaluation of large language models for patient education in refractive surgery.International journal of ophthalmology · 2026Article
- Evaluation of the readability, quality, and accuracy of AI chatbot responses to questions about deleterious oral habits.BMC oral health · 2025Article
- Performance of ChatGPT-4 Omni and Gemini 1.5 Pro on Ophthalmology-Related Questions in the Turkish Medical Specialty Exam.Turkish journal of ophthalmology · 2025Article
- Assessing ChatGPT's Educational Potential in Lung Cancer Radiotherapy From Clinician and Patient Perspectives: Content Quality and Readability Analysis.JMIR cancer · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To assess the appropriateness and readability of large language model (LLM) chatbots' answers to frequently asked questions about refractive surgery. Materials and Methods: Four commonly used LLM chatbots were asked 40 questions frequently asked by patients about refractive surgery. The appropriateness of the answers was evaluated by 2 experienced refractive surgeons. Readability was evaluated with 5 different indexes. Results: Based on the responses generated by the LLM chatbots, 45% (n=18) of the answers given by ChatGPT 3.5 were correct, while this rate was 52.5% (n=21) for ChatGPT 4.0, 87.5% (n=35) for Gemini, and 60% (n=24) for Copilot. In terms of readability, it was observed that all LLM chatbots were very difficult to read and required a university degree. Conclusion: These LLM chatbots, which are finding a place in our daily lives, can occasionally provide inappropriate answers. Although all were difficult to read, Gemini was the most successful LLM chatbot in terms of generating appropriate answers and was relatively better in terms of readability.
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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.