ArticleGastroenterology & hepatology2020
Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.
Article in Gastroenterology & hepatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Consensus for Operating Room Multimodal Data Management: Identifying Research Priorities for Data-Driven Surgery.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2024Article
- Convolutional neural network based data interpretable framework for Alzheimer's treatment planning.Visual computing for industry, biomedicine, and art · 2024Article
- Integrated multimodal artificial intelligence framework for healthcare applications.NPJ digital medicine · 2022Article
- Artificial Intelligence and the Future of Gastroenterology and Hepatology.Gastro hep advances · 2022Review
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 at 1 institution in 1 country.
Funding
Abstract
Artificial intelligence (AI) could change the practice of gastroenterology through its ability to both acquire and analyze information with speed, reproducibility, and, potentially, insight that may exceed that of human medical specialists. AI is powered by computational methods that allow machines to replicate clinical pattern recognition used by gastroenterology specialists to interpret endoscopic or cross-sectional images; understand the meaning and intent of medical documents; and merge different types of data to infer a diagnosis, prognosis, or expected outcome. Ongoing research is studying the use of AI for automated interpretation of text from colonoscopy and clinical documents for improved quality and patient phenotyping as well as enhanced detection and descriptions of polyps and other endoscopic lesions, and for predicting the probability of future therapeutic response early in a treatment course. This article introduces emerging technologies of natural language processing, machine vision, and machine learning for data analytics, and describes current and future applications in gastroenterology.
Indexed as
Identifiers
34035738PMC8132644W3165566310What 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.