ArticleRheumatology international2024
Assessing the accuracy and completeness of artificial intelligence language models in providing information on methotrexate use.
Article in Rheumatology international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it, 57 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Large Language Models for Chatbot Health Advice Studies: A Systematic Review.JAMA network open · 2025Pooled it
- Moderate-to-substantial agreement of ChatGPT-5 for Kellgren-Lawrence grading on synthetic knee radiographs: a controlled cross-sectional observer agreement study.Rheumatology international · 2026Article
- A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.Rheumatology international · 2026Observational
- Article
- Evaluation of Information Generated by ChatGPT on Preventing Peripheral Venous Catheter-Related Infections.Nursing open · 2026Article
- Evaluation of ChatGPT-4o and Gemini for gout management: a comparative analysis based on EULAR guidelines.Scientific reports · 2026Article
- Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study.JMIR medical informatics · 2025Article
- Comparative evaluation of large language models on multiple-choice and image-based rheumatology questions.Rheumatology international · 2025Article
- Evaluation of large language models in patient education for hyperthyroidism: A comparative study of chatgpt, gemini, and deepseek.Endocrine · 2025Article
- Artificial intelligence for autoimmune diseases.NPJ digital medicine · 2025Article
- Evaluating the performance of AI chatbots in responding to dental implant FAQs: A comparative study.BMC oral health · 2025Article
- Evaluation of ChatGPT-4's performance on pediatric dentistry questions: accuracy and completeness analysis.BMC oral health · 2025Article
- What Large Language Models offer about Familial Mediterranean Fever: An Analysis of Quality, Readability, Completeness, and Accuracy.Mediterranean journal of rheumatology · 2025Article
- Comparative analysis of large language models in providing patient information about keratoconus and contact lenses.International ophthalmology · 2025Article
- Emerging Artificial Intelligence Innovations in Rheumatoid Arthritis and Challenges to Clinical Adoption.Current rheumatology reports · 2025Review
- Evaluation of the Performance of Large Language Models in the Management of Axial Spondyloarthropathy: Analysis of EULAR 2022 Recommendations.Diagnostics (Basel, Switzerland) · 2025Article
- A comparative analysis of generative artificial intelligence responses from leading chatbots to questions about endometriosis.AJOG global reports · 2025Article
- Evaluating the Accuracy of Responses by Large Language Models for Information on Disease Epidemiology.Pharmacoepidemiology and drug safety · 2025Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to assess Large Language Models (LLMs)-ChatGPT 3.5-4, BARD, and Bing-in their accuracy and completeness when answering Methotrexate (MTX) related questions for treating rheumatoid arthritis. We employed 23 questions from an earlier study related to MTX concerns. These questions were entered into the LLMs, and the responses generated by each model were evaluated by two reviewers using Likert scales to assess accuracy and completeness. The GPT models achieved a 100% correct answer rate, while BARD and Bing scored 73.91%. In terms of accuracy of the outputs (completely correct responses), GPT-4 achieved a score of 100%, GPT 3.5 secured 86.96%, and BARD and Bing each scored 60.87%. BARD produced 17.39% incorrect responses and 8.7% non-responses, while Bing recorded 13.04% incorrect and 13.04% non-responses. The ChatGPT models produced significantly more accurate responses than Bing for the "mechanism of action" category, and GPT-4 model showed significantly higher accuracy than BARD in the "side effects" category. There were no statistically significant differences among the models for the "lifestyle" category. GPT-4 achieved a comprehensive output of 100%, followed by GPT-3.5 at 86.96%, BARD at 60.86%, and Bing at 0%. In the "mechanism of action" category, both ChatGPT models and BARD produced significantly more comprehensive outputs than Bing. For the "side effects" and "lifestyle" categories, the ChatGPT models showed significantly higher completeness than Bing. The GPT models, particularly GPT 4, demonstrated superior performance in providing accurate and comprehensive patient information about MTX use. However, the study also identified inaccuracies and shortcomings in the generated responses.
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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.