ReviewTranslational gastroenterology and hepatology2026
The role of artificial intelligence in gastroenterology: current perspectives and future directions-narrative review.
Review in Translational gastroenterology and hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- GeriAIGastroNet: AI-Assisted Gastrointestinal Polyp Segmentation and Severity-Based Triage for Tele-Gastroenterology in Underserved Geriatric Populations.Journal of clinical medicine · 2026Article
- GDF15-integrated blood biomarker panel for risk stratification of digestive malignancies: a multicenter retrospective study.Frontiers in pharmacology · 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
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Artificial intelligence (AI) has revolutionized the field of gastroenterology, leading to significant improvements in the diagnosis, management, and prognosis of several gastrointestinal (GI) disorders. With this context in mind, this brief review examines a wide range of subjects, including the history of AI in medicine and the state of AI in gastroenterology today, with a particular emphasis on its application in radiographic diagnosis, endoscopic procedures, disease detection, and clinical decision-making. Methods: A narrative review of the literature was conducted, encompassing studies published in English across major databases. The review covers historical developments of AI in medicine, contemporary AI applications in gastroenterology, and emerging trends. Key Content and Findings: AI techniques, including machine learning and deep learning, have demonstrated high accuracy in detecting GI pathologies such as polyps, neoplasms, inflammatory bowel disease, and other conditions. AI applications in endoscopy, video capsule endoscopy, and colonoscopy enable rapid analysis of large datasets, aiding early diagnosis and clinical decision-making. Challenges identified include data quality, model interpretability, ethical concerns, and liability associated with AI-assisted clinical decisions. Despite these challenges, AI continues to enhance gastroenterology practice and shows promise for broader clinical adoption. Conclusions: AI has significant potential to improve patient care in gastroenterology. Future advancements will require collaboration among AI developers, clinicians, and patients to address implementation barriers, optimize clinical utility, and inform policy and research directions.
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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.