A foundation systematic review of natural language processing applied to gastroenterology & hepatology
Abstract 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,...
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2025-02-01
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| Series: | BMC Gastroenterology |
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| Online Access: | https://doi.org/10.1186/s12876-025-03608-5 |
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| author | Matthew Stammers Balasubramanian Ramgopal Abigail Owusu Nimako Anand Vyas Reza Nouraei Cheryl Metcalf James Batchelor Jonathan Shepherd Markus Gwiggner |
| author_facet | Matthew Stammers Balasubramanian Ramgopal Abigail Owusu Nimako Anand Vyas Reza Nouraei Cheryl Metcalf James Batchelor Jonathan Shepherd Markus Gwiggner |
| author_sort | Matthew Stammers |
| collection | DOAJ |
| description | Abstract 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 between 2015 and 2023 that met the inclusion criteria. Studies lacking a description of appropriate validation or NLP methods were excluded, as were studies ufinavailable in English, those focused on non-gastrointestinal diseases and those that were 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%) on inflammatory bowel disease, 4 (7.4%) on gastroscopy, 4 (7.4%) on pancreatic disease and 2 (3.7%) on endoscopic sedation/ERCP and gastrointestinal bleeding. Only 30 (56.6%) of the studies reported patient demographics, and only 13 (24.5%) had a low risk of validation bias. Thirty-five (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 thus 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. |
| format | Article |
| id | doaj-art-3fa2296c423c4ef7a4715e25f982c9f6 |
| institution | Kabale University |
| issn | 1471-230X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Gastroenterology |
| spelling | doaj-art-3fa2296c423c4ef7a4715e25f982c9f62025-02-09T12:39:34ZengBMCBMC Gastroenterology1471-230X2025-02-0125111510.1186/s12876-025-03608-5A foundation systematic review of natural language processing applied to gastroenterology & hepatologyMatthew Stammers0Balasubramanian Ramgopal1Abigail Owusu Nimako2Anand Vyas3Reza Nouraei4Cheryl Metcalf5James Batchelor6Jonathan Shepherd7Markus Gwiggner8University Hospital SouthamptonUniversity Hospital SouthamptonUniversity Hospital SouthamptonUniversity Hospital SouthamptonClinical Informatics Research Unit (CIRU)University of SouthamptonClinical Informatics Research Unit (CIRU)Southampton Health Technologies Assessment Centre (SHTAC)University Hospital SouthamptonAbstract 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 between 2015 and 2023 that met the inclusion criteria. Studies lacking a description of appropriate validation or NLP methods were excluded, as were studies ufinavailable in English, those focused on non-gastrointestinal diseases and those that were 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%) on inflammatory bowel disease, 4 (7.4%) on gastroscopy, 4 (7.4%) on pancreatic disease and 2 (3.7%) on endoscopic sedation/ERCP and gastrointestinal bleeding. Only 30 (56.6%) of the studies reported patient demographics, and only 13 (24.5%) had a low risk of validation bias. Thirty-five (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 thus 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.https://doi.org/10.1186/s12876-025-03608-5ColonoscopyInflammatory bowel diseaseHepatocellular carcinomaGastroscopyPancreatic diseaseNatural language Processing |
| spellingShingle | Matthew Stammers Balasubramanian Ramgopal Abigail Owusu Nimako Anand Vyas Reza Nouraei Cheryl Metcalf James Batchelor Jonathan Shepherd Markus Gwiggner A foundation systematic review of natural language processing applied to gastroenterology & hepatology BMC Gastroenterology Colonoscopy Inflammatory bowel disease Hepatocellular carcinoma Gastroscopy Pancreatic disease Natural language Processing |
| title | A foundation systematic review of natural language processing applied to gastroenterology & hepatology |
| title_full | A foundation systematic review of natural language processing applied to gastroenterology & hepatology |
| title_fullStr | A foundation systematic review of natural language processing applied to gastroenterology & hepatology |
| title_full_unstemmed | A foundation systematic review of natural language processing applied to gastroenterology & hepatology |
| title_short | A foundation systematic review of natural language processing applied to gastroenterology & hepatology |
| title_sort | foundation systematic review of natural language processing applied to gastroenterology hepatology |
| topic | Colonoscopy Inflammatory bowel disease Hepatocellular carcinoma Gastroscopy Pancreatic disease Natural language Processing |
| url | https://doi.org/10.1186/s12876-025-03608-5 |
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