Evidence map›Paper›PMID 40974448›Full record

ArticleIndian journal of gastroenterology : official journal of the Indian Society of Gastroenterology2026

Evaluation of artificial intelligence-based patient education models for irritable bowel syndrome.

Anand Kumar Raghavendran, Balaji Musunuri, Siddheesh Rajpurohit, Ganesh Pai C, Shiran Shetty, Pretty Kumari, Rakshand Shetty, Athish Shetty, Ganesh Bhat

Abstract readEvaluation StudyComparative Study
PubMed Publisher
In one paragraph

Article in Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Anand Kumar RaghavendranDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Balaji MusunuriDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Siddheesh RajpurohitDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Ganesh Pai CDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Shiran ShettyDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Pretty KumariDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Rakshand ShettyDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Athish ShettyDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India.
Ganesh BhatDepartment of Gastroenterology and Hepatology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576 104, India. bhat.ganesh@manipal.edu.ORCID http://orcid.org/0000-0003-0707-3459

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIrritable bowel syndrome (IBS) is a common functional gastrointestinal disorder with a significant psycho-social burden. Despite medical advancements, patient education on IBS remains inadequate. This study compared two large language models (LLMs)-ChatGPT-4 and Gemini-1-for their performance in addressing IBS-related patient queries.

methodsThirty-nine IBS-related frequently asked questions (FAQs) from IBS organizations and hospital websites were categorized into six domains: general understanding, symptoms and diagnosis, causes, dietary considerations, treatment and lifestyle factors. Responses from ChatGPT-4 and Gemini-1 were evaluated by two independent gastroenterologists for comprehensiveness and accuracy, with a third reviewer resolving disagreements. Readability was measured using five standardized indices (Flesch Reading Ease [FRE], Simple Measure of Gobbledygook [SMOG], Gunning Fog Index [GFI], Automated Readability Index [ARI], Reading Level Consensus [ARC]) and empathy was rated on a 4-point Likert scale by three reviewers.

resultsGemini produced comprehensive and accurate answers for 94.9% (37/39) of questions, with two rated as mixed (vague/outdated). ChatGPT achieved 89.7% (35/39) comprehensive responses, with four rated mixed. Domain-wise, both models performed best in "symptoms and diagnosis" and "treatment", while mixed responses were most frequent in "general understanding" and "lifestyle". There was no significant difference in comprehensiveness (p = 0.67). Readability analysis showed both LLMs generated difficult-to-read content: Gemini's FRE score was 35.83 ± 3.31 vs. ChatGPT's 32.33 ± 5.57 (p = 0.21), corresponding to college-level proficiency. ChatGPT's responses were more empathetic, with all responses rated moderately empathetic; Gemini was mostly rated minimally empathetic (66.7%).

conclusionWhile ChatGPT and Gemini provided extensive information, their limitations-such as complex language and occasional inaccuracies-must be addressed. Future improvements should focus on enhancing readability, contextual relevance and accuracy to better meet the diverse needs of patients and clinicians.

Indexed as

Artificial IntelligenceIrritable Bowel SyndromePatient Education as TopicComprehensionGenerative Artificial IntelligenceHumansLarge Language ModelsArtificial intelligenceEmpathyHealth literacyIBSPatient education

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.