ArticleGastroenterology report2025
Revolutionizing inflammatory bowel disease healthcare communication: a head-to-head comparison of gastroenterologist and ChatGPT responses.
Article in Gastroenterology report, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence-driven large language models demonstrate immense potential in the medical field. It remains unclear whether ChatGPT has the ability to provide appropriate recommendations for patients with inflammatory bowel disease (IBD) that are comparable to those of gastroenterologists. This study quantitatively assessed the performance of ChatGPT's generated IBD-related recommendations from the distinct perspectives of gastroenterologists and patients. Methods: Healthcare questions regarding IBD were solicited from IBD patients and specialized physicians. Those questions were then presented to GPT-4 Omni and three independent senior gastroenterologists for responses. These responses were subsequently evaluated by a blinded panel of five board-certified gastroenterologists using a five-point Likert scale, assessing accuracy, completeness, and readability. Furthermore, 10 IBD patients as blinded assessors performed assessments of both ChatGPT's and gastroenterologists' responses. Results: Thirty high-frequency questions were selected, encompassing basic knowledge, treatment, and management domains. ChatGPT demonstrated high reproducibility in responding to these questions. Regarding accuracy and readability, ChatGPT's performance was comparable to that of gastroenterologists. For completeness of responses, ChatGPT outperformed gastroenterologists (4.42 ± 0.67 vs 4.19 ± 0.65; Conclusions: ChatGPT has the potential to provide stable, accurate, comprehensive, and comprehensible healthcare-related information for IBD patients. Further validation of the reliability and practicality of large language models in real-world clinical settings is crucial.
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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.