ReviewTherapeutic advances in gastroenterology2024
Large language models: a primer and gastroenterology applications.
Review in Therapeutic advances in gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled 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.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Patient-Representing Population's Perceptions of GPT-Generated Versus Standard Emergency Department Discharge Instructions: Randomized Blind Survey Assessment.Journal of medical Internet research · 2024Trial
- Large language models' performance in answering common patient questions about colonoscopy: an expert-based evaluation.BMC gastroenterology · 2026Observational
- Benchmarking Large Language Models in Acute-on-Chronic Pancreatitis: An Exploratory Study.Diagnostics (Basel, Switzerland) · 2026Article
- Therapeutic Decisions in Inflammatory Bowel Disease: ChatGPT-4.0 Compared to Medical Expertise.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Automatic processing of gastrointestinal endoscopy referrals and patient instructions using large language models.BMC gastroenterology · 2026Article
- Development and Validation of a Large Language Model Case Identification Strategy for Eosinophilic Esophagitis.Gastro hep advances · 2026Article
- Twelve tips for developing and implementing AI curriculum for undergraduate medical education.Medical education online · 2025Review
- Large language models in nephrology: applications and challenges in chronic kidney disease management.Renal failure · 2025Review
- Benchmarking proprietary and open-source language and vision-language models for gastroenterology clinical reasoning.NPJ digital medicine · 2025Article
- Perspectives of Patients Seeking Bariatric Surgery: The Impact of Early Patient-Provider Communication on Bariatric Surgery Utilization.Obesity surgery · 2025Article
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- Using Generative Artificial Intelligence in Health Economics and Outcomes Research: A Primer on Techniques and Breakthroughs.PharmacoEconomics - open · 2025Article
- Adoption of Large Language Model AI Tools in Everyday Tasks: Multisite Cross-Sectional Qualitative Study of Chinese Hospital Administrators.Journal of medical Internet research · 2025Article
- Fine-Tuning Large Language Models for Specialized Use Cases.Mayo Clinic proceedings. Digital health · 2025Review
- Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.Journal of clinical medicine · 2025Review
- Exploring the benefits and challenges of AI-driven large language models in gastroenterology: Think out of the box.Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.