ReviewWorld journal of gastroenterology2021
Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology.
Review in World journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Whole-slide deep-learning quantification of fibrosis and pathologist-defined proliferative/neoplastic regions in a thioacetamide-induced rat liver model.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026.Frontiers in oncology · 2026Review
- Artificial intelligence-driven gastrointestinal functional assessment: multimodal imaging, digital biomarkers, and real-time monitoring.Frontiers in physiology · 2026Review
- Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists' Visual Estimates.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer.Fundamental research · 2024Article
- An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.Cancer informatics · 2024Article
- Artificial intelligence in diagnostic pathology.Diagnostic pathology · 2023Review
- The pathobiology of follicular lymphoma.Journal of clinical and experimental hematopathology : JCEH · 2023Article
- Review
- The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews.Healthcare (Basel, Switzerland) · 2022Review
- Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm.Frontiers in oncology · 2022Article
- The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers.Computational and structural biotechnology journal · 2022Review
- AI-Powered histopathology slide image interpretation in oncology: A comprehensive knowledge mapping and bibliometric analysis.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology 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.