ReviewWorld journal of gastrointestinal endoscopy2024
Human-artificial intelligence interaction in gastrointestinal endoscopy.
Review in World journal of gastrointestinal endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses 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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN.Pediatric radiology · 2026Guideline
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- Advances in Artificial Intelligence for Gastrointestinal Endoscopy: 2026 Update.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Bridging innovation and clinical reality: Interpreting the comparative study of deep learning models for multi-class upper gastrointestinal disease segmentation.World journal of gastroenterology · 2026Article
- [Clinical Implementation of Artificial Intelligence in Endoscopy: A Human-Artificial Intelligence Interaction Perspective].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2026Review
- Colonoscopy Quality and Strategies for Improvement.Gut and liver · 2026Review
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.Translational gastroenterology and hepatology · 2026Review
- Human-machine collaboration in colorectal cancer screening: a narrative review of artificial intelligence-assisted colonoscopy.Frontiers in medicine · 2026Review
- Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025Review
- Role of Artificial Intelligence in Gastroenterology Training (2005-2025): Trends, Tools, and Challenges.Cureus · 2025Review
- Artificial intelligence-assisted colonoscopy for colorectal lesion detection: a case-control study on diagnostic accuracy and histopathological agreement.Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2025Article
- Augmented reality navigation systems in endoscopy.Frontiers in gastroenterology (Lausanne, Switzerland) · 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
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human-AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.
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.