Evidence map›Paper›PMID 41091774›Full record

ArticlePloS one2025

A prospective single-masked, non-inferiority, parallel-group randomized controlled trial of the efficacy of a ChatGPT-based AI chatbot to improve Boston bowel preparation scores for colonoscopy preparation: A trial protocol.

Nabil Mohammad Azmi, Muhammad Irfan Abdul Jalal, Siti Hamizah Mohd Ashar, Muhammad Irfan Mohd Nazri, Young Jie, Nagulan Ganeson, Joane K Augustine, Yew Sheng Qian

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06905782 (Prospective Single Blinded Randomized Control Trial on the Effectiveness of Using Large Language Model Artificial Intelligence Chatbot to Improve Boston Bowel Preparation Score), which is not on this map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

NCT06905782 nanot yet recruitingnot on this map

Prospective Single Blinded Randomized Control Trial on the Effectiveness of Using Large Language Model Artificial Intelligence Chatbot to Improve Boston Bowel Preparation Score (BBPS) for Colonoscopy Preparation

TypeinterventionalSponsorNational University of MalaysiaRan2025 to 2026Enrolled96ConditionsColorectal Cancer Screening, Colon Polyps and Adenomas, Bowel Preparation Quality, Artificial Intelligence (AI)ArmsAI chatbot study arm, Traditional counselling study arm
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Nabil Mohammad AzmiDepartment of Surgery, Faculty of Medicine, Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0009-0002-6129-3786
Muhammad Irfan Abdul JalalUKM Medical Molecular Biology Institute (UMBI), Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0001-6911-3335
Siti Hamizah Mohd AsharDepartment of Surgery, Faculty of Medicine, Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Muhammad Irfan Mohd NazriDepartment of Surgery, Faculty of Medicine, Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Young JieDepartment of Surgery, Faculty of Medicine, Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Nagulan GanesonDepartment of Surgery, Faculty of Medicine, Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Joane K AugustineDepartment of Surgery, Faculty of Medicine, Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Yew Sheng QianDepartment of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0002-2430-1540

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming healthcare through tools like large language model chatbots. AI chatbots can simulate human conversation, provide personalized information, and interact with patients in real time. Their ease of use and conversational interface make them attractive for healthcare education, especially in resource-limited settings. We propose a prospective, single-masked, randomized controlled trial to evaluate whether an AI-based chatbot (ChatGPT) is non-inferior to standard counseling in terms of patients' adherence to pre-colonoscopy bowel preparation instructions and thus enhance the Boston Bowel Preparation Score (BBPS). Patients undergoing colonoscopy (ntotal = 96) will be randomized to ChatGPT 4.0 Large Language Model (LLM)-aided Colonoscopy Counseling (NChatGPT = 48) or standard counseling (nsc = 48) arms at a 1:1 ratio using a central block randomization scheme of varying block sizes. In the first group, participants will interact with ChatGPT 4.0 for bowel preparation counseling before colonoscopy, whilst the second group will receive standard counseling from trained clinicians. Only the outcome assessors will be masked to the intervention allotment. The primary endpoint is the BBPS, assessed for non-inferiority. Secondary endpoints are patient anxiety (DASS-21) and patient satisfaction assessed using DASS-21 and PSQ-18 questionnaires, respectively and the findings will be reported descriptively with two-sided 95% confidence interval and any p-values will be considered exploratory without multiplicity adjustment. The primary endpoint data will be analyzed using the intention-to-treat (ITT) analysis and non-inferiority framework based on the analysis of covariance (ANCOVA) to control the confounders (age, gender (male as the risk factor), prior colonoscopy experience, colonoscopy indication, and baseline constipation score). The results will be compared with the findings based on the per-protocol (PP) analysis as part of the sensitivity analysis. The protocol adheres to SPIRIT 2025 and the SPIRIT-AI extension guidelines to ensure comprehensive reporting of this AI-based intervention. This trial has received ethics approval and the trial protocol has been registered with the clinicaltrials.gov registry (NCT06905782).

Indexed as

Artificial IntelligenceColonoscopyAdultCatharticsCounselingEquivalence Trials as TopicFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedProspective StudiesRandomized Controlled Trials as TopicSingle-Blind MethodCathartics

Identifiers

PMID41091774
PMCPMC12527164

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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.