ArticleDigestive diseases and sciences2026
Readability, Quality, Understandability, and Actionability of ChatGPT Generated GI Patient Education Versus AGA Patient Center.
Article in Digestive diseases and sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Comment on "Readability, Quality, Understandability, and Actionability of ChatGPT-Generated GI Patient Education Versus AGA Patient Center".Digestive diseases and sciences · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsPatients increasingly use the internet and artificial intelligence chatbots to obtain health information, yet the readability, quality, understandability, and actionability of AI-generated gastrointestinal patient education remain unclear. This study compared gastrointestinal patient education from a professional society website with content generated by ChatGPT using validated health literacy instruments.
methodsIn this cross-sectional comparative study, 50 gastrointestinal patient education topics from the American Gastroenterological Association patient information website were paired with ChatGPT-generated responses using standardized prompts. Readability was assessed using the Flesch-Kincaid Grade Level, quality of treatment information was evaluated using the DISCERN instrument, and understandability and actionability were assessed using the Patient Education Materials Assessment Tool; scoring was performed by two blinded reviewers. Paired t tests were used to compare mean scores between sources, and intraclass correlation coefficients (ICCs) were used to assess interrater reliability between reviewers.
resultsFifty paired topics were analyzed. The mean Flesch-Kincaid Grade Level was higher for ChatGPT than GI website materials (10.33 ± 1.5 vs 8.72 ± 1.7; mean difference, 1.61; P < .001). Differences in DISCERN scores (63.5 ± 5.7 vs 64.3 ± 5.4; mean difference, - 0.8), PEMAT understandability (87.9% ± 6.9% vs 86.5% ± 7.8%; mean difference, 1.4%; P = .33), and PEMAT actionability (78.6% ± 9.8% vs 77.9% ± 10.2%; mean difference, 0.6%; P = .73) were not statistically significant. Inter-rater reliability was excellent across all measures, with intraclass correlation coefficients of 0.97 (95% CI, 0.95-0.99) for PEMAT understandability, 0.96 (95% CI, 0.94-0.98) for PEMAT actionability, and 0.99 (95% CI, 0.98-0.99) for DISCERN.
conclusionChatGPT-generated gastrointestinal patient education demonstrated similar quality, understandability, and actionability compared with professional society materials but was written at a significantly higher reading level. Improving readability may enhance accessibility and support the safe integration of AI-generated patient education.
Indexed as
Identifiers
What OpenQuestion holds
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.