Artificial Intelligence Tools for Gastrointestinal Research: A Practical Guide
Clinical Snapshot
PICO Framework
| P — Population | Gastrointestinal researchers and clinicians engaged in biomedical manuscript preparation |
| I — Intervention | Integration of large language model (LLM) AI tools (e.g., ChatGPT, Claude, Gemini) across stages of manuscript writing workflow |
| C — Comparator | No formal comparator; narrative review without a control condition |
| O — Outcomes | Practical guidance for responsible AI use in GI research manuscript preparation; structured step-by-step workflow recommendations |
Bottom Line
This narrative review from the University of North Carolina offers a structured, accessible guide for GI researchers seeking to incorporate LLM-based AI tools into their manuscript writing workflow. While the topic is timely and the specialty-specific framing is useful, the paper's evidentiary foundation is weak. It is a consensus-based opinion piece without systematic methodology, empirical validation, or formal risk-benefit analysis. The recommendations carry face validity but cannot be considered evidence-based in the CEBM sense. Senior clinicians and researchers should treat this as a starting-point framework rather than definitive guidance. Critical gaps include inadequate treatment of LLM hallucination risks, data confidentiality concerns when uploading unpublished research, authorship accountability, and the rapid obsolescence of tool-specific advice. Australian researchers must additionally ensure compliance with NHMRC research integrity standards and institutional AI policies, which vary considerably across universities and health networks. The paper's value lies in raising awareness and initiating structured conversation about responsible AI use in GI research — not in providing validated, reproducible protocols. Independent institutional guidance and ongoing critical engagement with this literature remain essential.
Key Findings
P Value: Not reported
Effect Size: Not applicable — no quantitative effect size reported; qualitative guidance document
Primary Outcome: Structured practical guidance for responsible integration of LLM-based AI tools across stages of GI research manuscript preparation
Nnt Or Sensitivity: Not applicable — no therapeutic, diagnostic, or prognostic outcome data presented; no NNT, sensitivity, specificity, or hazard ratio calculable
Confidence Interval: Not reported — narrative review with no statistical analysis
Clinical Application
Recommendations are broadly feasible for researchers with access to internet-based LLM platforms. However, feasibility varies by institutional policy on AI use, availability of premium tool subscriptions, researcher digital literacy, and journal-specific AI disclosure requirements. Implementation in resource-limited settings or institutions with strict data governance policies may be constrained. Australian GI researchers should note that AI tool use in manuscript preparation must align with NHMRC Australian Code for the Responsible Conduct of Research (2018) and individual institutional research integrity policies. The Australian and New Zealand Gastroenterology Society (GESA) has not, at time of appraisal, issued specific AI guidance for GI researchers. Researchers should consult target journal policies — many journals indexed in MEDLINE now require explicit AI use disclosure per ICMJE recommendations. The TGA does not regulate AI writing tools, but researchers using AI to assist in clinical trial reporting or regulatory submissions should exercise particular caution regarding accuracy and accountability. PBS and TGA considerations are not directly relevant to this methodological guidance paper, though AI-assisted systematic reviews informing PBS submissions would require the highest standards of transparency and reproducibility not guaranteed by narrative AI-assisted workflows. Academic gastroenterologists, hepatologists, GI trainees, and allied health researchers engaged in biomedical manuscript preparation in any research-active setting
Abstract
Artificial intelligence (AI) tools, including large language models (LLMs) such as ChatGPT, Claude, and Gemini, are increasingly used in biomedical research. However, practical guidance for integrating these tools into the manuscript writing workflow remains limited. This narrative review provides a structured, step-by-step guide for gastrointestinal (GI) researchers and clinicians seeking to use AI responsibly across the stages of manuscript preparation.
References
- 1.Gainey, C. S., Shroff, H., & Fix, O. K. (2026). Artificial intelligence tools for gastrointestinal research: A practical guide. Clinical Gastroenterology and Hepatology, advance online publication. https://doi.org/10.1016/j.cgh.2026.03.032
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