ReviewNPJ digital medicine2022
Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- AI Revolution in Upper GI: Endoscopic Diagnosis of Gastric Cancers and Premalignant Neoplasms.Diseases (Basel, Switzerland) · 2026Review
- A Dataset for Depth in Robotic Endoscopy with Dynamic Scenarios (DRENDS).Scientific data · 2026Article
- Comment on the clinical trial landscape of artificial intelligence applications in gastrointestinal endoscopy.International journal of surgery (London, England) · 2026Article
- Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.Nature biomedical engineering · 2026Article
- InfoColon: A dataset for consecutive informative frames in Colonoscopy.Scientific data · 2026Article
- Advances and Opportunities in NIR-II Endoscopy: From Diagnosis to Therapeutic Applications.Diagnostics (Basel, Switzerland) · 2026Review
- Automated kidney stone segmentation from flexible ureteroscopy videos using a U-net model: A preliminary feasibility study.Scientific reports · 2026Article
- Gastrointestinal endoscopy for physiological assessment in functional gastrointestinal disorders: a perspective.Frontiers in medicine · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy: Current Advances, Clinical Integration, and Future Directions: A Narrative Review.Clinical and experimental gastroenterology · 2026Review
- Probe-based confocal laser endomicroscopy: progress, challenges, and emerging applications.Surgical endoscopy · 2025Review
- Clinical validation of an AI-assisted system for real-time kidney stone detection during flexible ureteroscopic surgery.NPJ digital medicine · 2025Article
- Automated endoscopic image quality assessment for robust colorectal lesion detection.BMC medical imaging · 2025Article
- Classification and Segmentation of Mucus Morphology Using Deep Learning During Diagnostic Nasal Endoscopy.International forum of allergy & rhinology · 2025Article
- Artificial Intelligence in Pancreatobiliary Endoscopy: Current Advances, Opportunities, and Challenges.Journal of clinical medicine · 2025Review
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- Survey on the perceptions of Asian endoscopists to artificial intelligence.Communications medicine · 2025Article
- How machine learning on real world clinical data improves adverse event recording for endoscopy.NPJ digital medicine · 2025Article
- A hybrid XAI-driven deep learning framework for robust GI tract disease diagnosis.Scientific reports · 2025Article
- Evaluation of Perceptual Realism and Clinical Plausibility of AI-Generated Colon Polyp Images.Biomedicines · 2025Article
- Multiclassification of Colorectal Polyps from Colonoscopy Images Using AI for Early Diagnosis.Diagnostics (Basel, Switzerland) · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.
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.