ReviewSurgery today2022
Machine learning in gastrointestinal surgery.
Review in Surgery today, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neurosurgical skills analysis by machine learning models: systematic review.Neurosurgical review · 2023Pooled it
- Predictors of same-day discharge following bariatric surgery using machine learning.Surgical endoscopy · 2026Article
- Article
- Explainable Machine Learning for Early Prediction of Surgical Necessity in Gastrointestinal Emergencies: A Multimodal Diagnostic Study.Diagnostics (Basel, Switzerland) · 2026Article
- Time-sequential prediction of postoperative complications after gastric cancer surgery using machine learning: a multicenter cohort study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Artificial intelligence in gastrointestinal surgery: A systematic review.World journal of gastrointestinal surgery · 2025Article
- Effect of perioperative disinfection and isolation measures in infection control after gastrointestinal surgery: A retrospective analysis.World journal of gastrointestinal surgery · 2025Article
- Machine learning for modeling and identifying risk factors of pancreatic fistula.World journal of gastrointestinal oncology · 2025Article
- Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection.Biomedicines · 2024Article
- Application of Machine Learning Based on Structured Medical Data in Gastroenterology.Biomimetics (Basel, Switzerland) · 2023Review
- Technological advancements in surgical laparoscopy considering artificial intelligence: a survey among surgeons in Germany.Langenbeck's archives of surgery · 2023Article
- The use and future perspective of Artificial Intelligence-A survey among German surgeons.Frontiers in public health · 2022Article
- Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis.Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.
Indexed as
Identifiers
34559310What 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.