Bharadwaj, H.R., Dahiya, D.S., Dalal, P. et al. (10 more authors) (2025) Artificial intelligence in population-level gastroenterology and hepatology: a comprehensive review of public health applications and quantitative impact. Digestive Diseases and Sciences. ISSN: 0163-2116
Abstract
Background
Artificial intelligence (AI), which includes machine learning and deep learning, is fundamentally changing public health in gastroenterology and hepatology—fields grappling with a significant global disease burden.
Objective
This review focuses on the population-level applications and impact of AI, highlighting its role in shifting healthcare strategies from reactive treatment to proactive prevention.
Results
AI demonstrates substantial improvements across many different areas. In colorectal cancer, AI models significantly boost detection rates, successfully identifying a large majority of high-risk individuals often missed by traditional screening methods. For metabolic dysfunction-associated steatotic liver disease (MASLD), advanced non-invasive tests offer a high degree of reliability in detecting liver fibrosis. The identification of viral hepatitis is enhanced with excellent accuracy, and gastrointestinal infection surveillance benefits from wastewater analysis that provides an early warning system weeks ahead of clinical case reporting. Furthermore, AI improves the diagnosis of upper GI cancers, such as gastric cancer, with higher diagnostic capability, and facilitates precision public health in inflammatory bowel disease (IBD) through highly accurate risk prediction models.
Challenges
Despite these important advances, significant hurdles remain. Key challenges include ensuring diverse and representative data to prevent algorithmic bias, protecting patient privacy, establishing robust regulatory frameworks for new technologies, and successfully moving innovations from research settings into practical, real-world deployment.
Conclusion
The unequal distribution of AI development and access between high-income countries and low- and middle-income countries risks exacerbating existing health disparities. To fully realize AI's transformative potential for global public health in gastroenterology and hepatology, these cross-cutting issues must be actively addressed through ethical design, rigorous validation, and equitable worldwide deployment.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Artificial intelligence; Gastroenterology; Hepatology; Machine learning; Public health |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
| Date Deposited: | 05 Nov 2025 16:46 |
| Last Modified: | 05 Nov 2025 16:46 |
| Status: | Published online |
| Publisher: | Springer Science and Business Media LLC |
| Refereed: | Yes |
| Identification Number: | 10.1007/s10620-025-09452-7 |
| Related URLs: | |
| Sustainable Development Goals: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:234022 |
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Filename: s10620-025-09452-7.pdf
Licence: CC-BY 4.0


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