ReviewGastroenterology research2025
Large Language Models in Gastroenterology and Gastrointestinal Surgery: A New Frontier in Patient Communication and Education.
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.
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.
- The role of large language models in the writing of surgical reviews: fact or fantasy?Langenbeck's archives of surgery · 2026Article
- Arkangel AI, OpenEvidence, ChatGPT, Medisearch: Are They Objectively up to Medical Standards? A Real-Life Assessment of LLM Chatbots in Health Care.Mayo Clinic proceedings. Digital health · 2026Article
- Article
- Efficacy of Large Language Models in Providing Evidence-Based Patient Education for Celiac Disease: A Comparative Analysis.Nutrients · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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.