Evidence map›Paper›PMID 40322195›Full record

ReviewGastroenterology research2025

Large Language Models in Gastroenterology and Gastrointestinal Surgery: A New Frontier in Patient Communication and Education.

Dushyant Singh Dahiya, Hassam Ali, Vishali Moond, M Danial Ali Shah, Christina Santana, Noor Ali, Abu Baker Sheikh, Muhammad Ahmad Nadeem, Aqsa Munir, Mohammed A Quazi and 2 more

Abstract readReview
In one paragraph

Review in Gastroenterology research, 2025. 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. Article
  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

12 authors.

Dushyant Singh DahiyaDivision of Gastroenterology, Hepatology and Motility, The University of Kansas School of Medicine, Kansas City, KS, USA.ORCID https://orcid.org/0000-0002-8544-9039
Hassam AliDepartment of Gastroenterology, Division of Internal Medicine, ECU Health Medical Center/Brody School of Medicine, Greenville, NC, USA.
Vishali MoondDepartment of Internal Medicine, Saint Peter's University Hospital/Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
M Danial Ali ShahDepartment of Internal Medicine, King Edward Medical University, Lahore, Pakistan.
Christina SantanaDepartment of Internal Medicine, Division of Internal Medicine, ECU Health Medical Center/Brody School of Medicine, Greenville, NC, USA.
Noor AliDepartment of Public Health, East Carolina University, Greenville, NC, USA.
Abu Baker SheikhDepartment of Internal Medicine, University of New Mexico Health Sciences, Albuquerque, NM, USA.
Muhammad Ahmad NadeemDepartment of Hepato-Pancreato-Biliary and Liver Transplant Surgery, Digestive Diseases and Surgery Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Aqsa MunirDepartment of Internal Medicine, Dow Medical College, Karachi, Pakistan.
Mohammed A QuaziDepartment of Internal Medicine, University of New Mexico Health Sciences, Albuquerque, NM, USA.
Hareesha Rishab BharadwajFaculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Amir Humza SohailDepartment of Surgery, University of New Mexico Health Sciences, Albuquerque, NM, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When integrated into healthcare, large language models (LLMs) have transformative and revolutionary effects, including significant potential for improving patient care and streamlining clinical processes. However, one specialty that particularly requires data on LLM use is gastroenterology and gastrointestinal surgery, a gap we sought to address in our research. Advanced artificial intelligence (AI) systems like LLMs have demonstrated the ability to mimic human communication, assist in diagnosis, provide patient education, and support medical research simultaneously. Despite these advantages, challenges such as biases, data privacy concerns, and lack of transparency in decision-making remain critical. The role of regulations in mitigating these risks is widely debated, with proponents advocating for structured oversight to enhance trust and patient safety, while others caution against potential barriers to innovation. Rather than replacing human expertise, AI should be integrated thoughtfully to complement clinical decision-making. Ensuring a balanced approach requires collaboration between medical professionals, AI developers, and policymakers to optimize its responsible implementation in healthcare.

Indexed as

Artificial intelligenceEthical medical careGastroenterologyGastroenterology surgeryHealthcare

Identifiers

PMID40322195
PMCPMC12045793

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.