ArticleWorld journal of gastroenterology2025
Evaluating large language models as patient education tools for inflammatory bowel disease: A comparative study.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Performance of large language models in mitral valve surgery patient education: a comparative analysis.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in inflammatory bowel disease: bridging innovation, implementation and impact.Nature reviews. Gastroenterology & hepatology · 2026Review
- Responsible use of large language models in gastroenterology and hepatology.Therapeutic advances in gastroenterology · 2026Review
- Benchmarking public large language model responses to patient-facing inflammatory bowel disease questions: informational quality, transparency proxies, and readability.Frontiers in public health · 2026Article
- Efficacy of Large Language Models in Providing Evidence-Based Patient Education for Celiac Disease: A Comparative Analysis.Nutrients · 2025Article
- DeepGut: A collaborative multimodal large language model framework for digestive disease assisted diagnosis and treatment.World journal of gastroenterology · 2025Observational
- Assessing ChatGPT-v4 for Guideline-Concordant Inflammatory Bowel Disease: Accuracy, Completeness, and Temporal Drift.Journal of clinical medicine · 2025Article
- Advancing large language models as patient education tools for inflammatory bowel disease.World journal of gastroenterology · 2025Article
- Evaluation of accuracy, quality, and readability of information on hypothyroidism provided by different artificial intelligence chatbot models.Frontiers in public health · 2025Article
- Artificial intelligence in patient education: evaluating large language models for understanding rheumatology literature.Frontiers in digital health · 2025Article
- Comparative performance of ChatGPT and DeepSeek in interpreting the 2025 ESICM guidelines on sepsis fluid therapy.Digital healthArticle
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Authors and funding
9 authors.
Funding
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Abstract
backgroundInflammatory bowel disease (IBD) is a global health burden that affects millions of individuals worldwide, necessitating extensive patient education. Large language models (LLMs) hold promise for addressing patient information needs. However, LLM use to deliver accurate and comprehensible IBD-related medical information has yet to be thoroughly investigated.
aimTo assess the utility of three LLMs (ChatGPT-4.0, Claude-3-Opus, and Gemini-1.5-Pro) as a reference point for patients with IBD.
methodsIn this comparative study, two gastroenterology experts generated 15 IBD-related questions that reflected common patient concerns. These questions were used to evaluate the performance of the three LLMs. The answers provided by each model were independently assessed by three IBD-related medical experts using a Likert scale focusing on accuracy, comprehensibility, and correlation. Simultaneously, three patients were invited to evaluate the comprehensibility of their answers. Finally, a readability assessment was performed.
resultsOverall, each of the LLMs achieved satisfactory levels of accuracy, comprehensibility, and completeness when answering IBD-related questions, although their performance varies. All of the investigated models demonstrated strengths in providing basic disease information such as IBD definition as well as its common symptoms and diagnostic methods. Nevertheless, when dealing with more complex medical advice, such as medication side effects, dietary adjustments, and complication risks, the quality of answers was inconsistent between the LLMs. Notably, Claude-3-Opus generated answers with better readability than the other two models.
conclusionLLMs have the potential as educational tools for patients with IBD; however, there are discrepancies between the models. Further optimization and the development of specialized models are necessary to ensure the accuracy and safety of the information provided.
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