ArticleReumatologia2025
Evaluating ChatGPT-4 for rheumatology patient education: a comparative analysis of readability, reliability, and similarity to the American College of Rheumatology's fact sheets.
Article in Reumatologia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- Artificial intelligence in rheumatology: a cross-sectional Scopus-based analysis.Rheumatology international · 2026Article
- Comparative evaluation of large language model-based AI platforms for radiographic assessment in rheumatoid arthritis.Rheumatology international · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: This study aimed to evaluate the readability, quality, reliability, similarity, and length of texts generated by ChatGPT on common rheumatic diseases and compare their content with American College of Rheumatology (ACR) patient education fact sheets. Material and methods: Fifteen common rheumatic diseases were included based on the ACR fact sheets. Questions about disease characteristics, symptoms, treatments, and lifestyle recommendations were generated based on ACR content and input into ChatGPT-4 for comparison. Readability was assessed using the Flesch-Kincaid Grade Level (FKGL), Flesch Reading Ease (FRE), and the Simple Measure of Gobbledygook (SMOG) index. Quality and reliability were evaluated using the DISCERN questionnaire and the Ensuring Quality Information for Patients (EQIP) tool. Text similarity was measured using cosine similarity, and word count was obtained using Microsoft Word. Results: ChatGPT-generated texts had significantly higher FKGL scores (14.3 vs. 12.7; Conclusions: Despite the moderate similarity, ChatGPT-generated texts on rheumatic diseases were more complex, less reliable, and longer than ACR fact sheets. These findings highlight the need for improvements in artificial intelligence-driven healthcare tools to ensure readability, accuracy, and reliability, making them more aligned with expert-reviewed resources.
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Registered trials
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