ReviewArchives of academic emergency medicine2019
Applications of Machine Learning Approaches in Emergency Medicine; a Review Article.
Review in Archives of academic emergency medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 6 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
45 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Exploring an AI-driven dynamic triage system for real-time patient risk reassessment in emergency departments in low-resource settings.Frontiers in digital health · 2026Pooled it
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Pooled it
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2024Pooled it
- Pooled it
- Machine Learning in Medical Emergencies: a Systematic Review and Analysis.Journal of medical systems · 2021Pooled it
- Machine learning for risk stratification in the emergency department (MARS-ED): a randomized controlled trial.Nature communications · 2025Trial
- Comparison of simulation and video-based training for acute asthma.BMC medical education · 2023Trial
- Early prediction of pressure injury risk in hospitalized patients using supervised machine learning models based on nursing records.Scientific reports · 2026Article
- Article
- Prediction of infection in the emergency department-a machine learning model.Frontiers in artificial intelligence · 2026Article
- Article
- Role of artificial intelligence in virtual emergency care: a protocol for a systematic review.BMJ open · 2025Article
- Artificial intelligence vs. emergency physicians: who diagnoses better?Revista da Associacao Medica Brasileira (1992) · 2025Article
- Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review.Archives of academic emergency medicine · 2025Article
- A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework.Health care management science · 2024Article
- Improving Triage Accuracy in Prehospital Emergency Telemedicine: Scoping Review of Machine Learning-Enhanced Approaches.Interactive journal of medical research · 2024Article
- Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Article
- Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction.Frontiers in digital health · 2024Article
- Enhancing bladder cancer diagnosis through transitional cell carcinoma polyp detection and segmentation: an artificial intelligence powered deep learning solution.Frontiers in artificial intelligence · 2024Article
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
Using artificial intelligence and machine learning techniques in different medical fields, especially emergency medicine is rapidly growing. In this paper, studies conducted in the recent years on using artificial intelligence in emergency medicine have been collected and assessed. These studies belonged to three categories: prediction and detection of disease; prediction of need for admission, discharge and also mortality; and machine learning based triage systems. In each of these categories, the most important studies have been chosen and accuracy and results of the algorithms have been briefly evaluated by mentioning machine learning techniques and used datasets.
Indexed as
Identifiers
31555764PMC6732202What 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.