Objective: This review assesses the progress of NLP in gastroenterology to date, grades the robustness of the methodology, exposes the field to a new generation of authors, and highlights opportunities for future research.Design: Seven scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, Pubmed, Scopus and Google Scholar) were searched for studies published 2015–2023 meeting inclusion criteria. Studies lacking a description of appropriate validation or NLP methods were excluded, as were studies unavailable in English, focused on non-gastrointestinal diseases and duplicates. Two independent reviewers extracted study information, clinical/algorithm details, and relevant outcome data. Methodological quality and bias risks were appraised using a checklist of quality indicators for NLP studies.Results: Fifty-three studies were identified utilising NLP in Endoscopy, Inflammatory Bowel Disease, Gastrointestinal Bleeding, Liver and Pancreatic Disease. Colonoscopy was the focus of 21(38.9%) studies, 13(24.1%) focused on liver disease, 7(13.0%) inflammatory bowel disease, 4(7.4%) on gastroscopy, 4(7.4%) on pancreatic disease and 2(3.7%) studies focused on endoscopic sedation/ERCP and gastrointestinal bleeding respectively. Only 30(56.6%) of studies reported any patient demographics, and only 13(24.5%) scored as low risk of validation bias. 35(66%) studies mentioned generalisability but only 5(9.4%) mentioned explainability or shared code/models.Conclusion: NLP can unlock substantial clinical information from free-text notes stored in EPRs and is already being used, particularly to interpret colonoscopy and radiology reports. However, the models we have so far lack transparency, leading to duplication, bias, and doubts about generalisability. Therefore, greater clinical engagement, collaboration, and open sharing of appropriate datasets and code are needed.
