ReviewBiomimetics (Basel, Switzerland)2023
Application of Machine Learning Based on Structured Medical Data in Gastroenterology.
Review in Biomimetics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI in Esophageal Motility Disorders: Systematic Review of High-Resolution Manometry Studies.Journal of medical Internet research · 2025Pooled it
- Large Language Models in Gastroenterology: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Interpretable, internally validated prognostic modelling of cumulative clinical pregnancy in women with diminished ovarian reserve.BMC medical informatics and decision making · 2026Article
- Machine learning for prognostic assessment in elderly patients with gastroduodenal perforation.BMC surgery · 2026Article
- Domain specific multimodal large language model for automated endoscopy reporting with multicenter prospective validation.NPJ digital medicine · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- Multimodal deep learning integration for predicting renal function outcomes in living donor kidney transplantation: a retrospective cohort study.International journal of surgery (London, England) · 2026Article
- What is the role of large language models in the management of urolithiasis?: a review.Urolithiasis · 2025Review
- New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.Scientific reports · 2025Article
- Harnessing multi-omics and artificial intelligence: revolutionizing prognosis and treatment in hepatocellular carcinoma.Frontiers in immunology · 2025Article
- The Potential Clinical Utility of the Customized Large Language Model in Gastroenterology: A Pilot Study.Bioengineering (Basel, Switzerland) · 2024Article
- Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection.Computational and structural biotechnology journal · 2024Article
- Advancements and challenges in gastrointestinal imaging.World journal of clinical cases · 2024Article
- Revolutionizing gastrointestinal endoscopy: the emerging role of large language models.Clinical endoscopy · 2024Article
- Evaluating the role of large language models in inflammatory bowel disease patient information.World journal of gastroenterology · 2024Article
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.Healthcare (Basel, 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.
Funding
Abstract
The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.
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.