ReviewDigestive diseases and sciences2026
Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.
Review in Digestive diseases and sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold.European journal of clinical investigation · 2026Review
- Establishing Equity and Validation Standards Before Widespread AI Adoption in Gastroenterology.Digestive diseases and sciences · 2026Article
- Identification of senescence-related genes as diagnostic biomarkers for gastric cancer using bioinformatics and machine learning.Discover oncology · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
objectiveThis review focuses on the population-level applications and impact of AI, highlighting its role in shifting healthcare strategies from reactive treatment to proactive prevention.
resultsAI 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.
conclusionThe 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.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.