Evidence map›Paper›PMID 41136718›Full record

ReviewDigestive diseases and sciences2026

Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.

Hareesha Rishab Bharadwaj, Dushyant Singh Dahiya, Priyal Dalal, Muhtasim Fuad, Hafiz Ali Raza, Muhammad Ibrahim, Arkadeep Dhali, Fariha Hasan, Balamrit Singh Sokhal, Karan Yagnik and 3 more

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Hareesha Rishab Bharadwaj *Royal Stoke University Hospital, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, ST4 6QG, UK.
Dushyant Singh Dahiya *Division of Gastroenterology, Hepatology & Motility, University of Kansas School of Medicine, Kansas City, KS, 67214, USA.
Priyal DalalSchool of Medicine, The University of Central Lancashire, Preston, PR1 2HE, UK.
Muhtasim FuadRoyal Stoke University Hospital, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent, ST4 6QG, UK.
Hafiz Ali RazaFaisalabad Medical University, Faisalabad, 38000, Pakistan.
Muhammad IbrahimBannu Medical College, Bannu, 02816, Pakistan.
Arkadeep DhaliAcademic Unit of Gastroenterology, Royal Hallamshire Hospital, Sheffield, S10 2JF, UK.
Fariha HasanDepartment of Internal Medicine, Cooper University Hospital, Camden, NJ, 08103, USA.
Balamrit Singh SokhalRoyal Stoke University Hospital, Stoke-on-Trent, ST4 6QG, UK.
Karan YagnikDepartment of Internal Medicine, Rutgers Health/Monmouth Medical Center, Long Branch, NJ, 07740, USA.
Bhanu Siva Mohan PinnamDivision of Gastroenterology, Hepatology & Motility, University of Kansas School of Medicine, Kansas City, KS, 67214, USA.
Farhan GoharRoyal Stoke University Hospital, Stoke-on-Trent, ST4 6QG, UK.
Hassam AliDivision of Gastroenterology, Hepatology & Nutrition, Brody School of Medicine, Greenville, NC, 27834, USA. alih20@ecu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Artificial IntelligenceGastroenterologyGastrointestinal DiseasesLiver DiseasesPublic HealthHumansArtificial intelligenceGastroenterologyHepatologyMachine learningPublic health

Identifiers

PMID41136718
PMCPMC13144179

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.