ReviewDiagnostics (Basel, Switzerland)2022
Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 35 citations in OpenAlex.
- Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.Scientific reports · 2026Article
- Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.BMC gastroenterology · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- World Endoscopy Organization Position Statements for Artificial Intelligence in Endoscopic Diagnosis of Gastric Epithelial Neoplasia.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Review
- Cut instance mixing: A domain-specific data augmentation method applied to gastrointestinal lesion detection.Scientific reports · 2026Article
- Current Role of Artificial Intelligence in the Management of Gastric Cancer.Biomedicines · 2025Review
- Artificial Intelligence in Upper Gastrointestinal Endoscopy: Current Evidence, Practice, and Future Directions.Cureus · 2025Review
- Artificial Intelligence in Upper Gastrointestinal Diagnosis.The Korean journal of helicobacter and upper gastrointestinal research · 2025Review
- GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach.Scientific data · 2025Article
- Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.Diagnostics (Basel, Switzerland) · 2024Article
- Real-World Application of Artificial Intelligence for Detecting Pathologic Gastric Atypia and Neoplastic Lesions.Journal of gastric cancer · 2024Article
- The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- Vision transformer architecture and applications in digital health: a tutorial and survey.Visual computing for industry, biomedicine, and art · 2023Review
- A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models.Diagnostics (Basel, Switzerland) · 2023Article
- Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.NPJ digital medicine · 2022Review
- An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer.Cancers · 2022Article
- Artificial intelligence in gastrointestinal endoscopy: a comprehensive review.Annals of gastroenterologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.
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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.