ReviewJournal of clinical medicine2021
Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature.
Review in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- Comparison Between Same-Day and Split-Dose Preparations with Sodium Picosulfate/Magnesium Citrate: A Randomized Noninferiority Study.Digestive diseases and sciences · 2022Trial
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.BMC medical informatics and decision making · 2025Article
- A fusion model of manually extracted visual features and deep learning features for rebleeding risk stratification in peptic ulcers.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Genotoxic effects of NDMA-contaminated ranitidine on Allium cepa cells and unveiling carcinogenic mechanisms via DFT and molecular dynamics simulation study.Scientific reports · 2024Article
- An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Chemoprevention of Colitis-Associated Dysplasia or Cancer in Inflammatory Bowel Disease.Gut and liver · 2022Review
- Machine-Learning Algorithm for Predicting Fatty Liver Disease in a Taiwanese Population.Journal of personalized medicine · 2022Article
- Forrest Classification for Bleeding Peptic Ulcer: A New Look at the Old Endoscopic Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Endoscopic Resection for Gastric Subepithelial Tumor with Backup Laparoscopic Surgery: Description of a Single-Center Experience.Journal of clinical medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the decreasing incidence of peptic ulcer bleeding (PUB) over the past two decades, the clinician experience of managing patients with PUB has also declined, especially for young endoscopists. A patient with PUB management requires collaborative care involving the emergency department, gastroenterologist, radiologist, and surgeon, from initial assessment to hospital discharge. The application of artificial intelligence (AI) methods has remarkably improved people's lives. In particular, AI systems have shown great potential in many areas of gastroenterology to increase human performance. Colonoscopy polyp detection or diagnosis by an AI system was recently introduced for commercial use to improve endoscopist performance. Although PUB is a longstanding health problem, these newly introduced AI technologies may soon impact endoscopists' clinical practice by improving the quality of care for these patients. To update the current status of AI application in PUB, we reviewed recent relevant literature and provided future perspectives that are required to integrate such AI tools into real-world practice.
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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.