ReviewTurkish journal of emergency medicine
Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.
Review in Turkish journal of emergency medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Comparing the text-based diagnostic reasoning performance of emergency medicine physicians and large language models in both definitive and differential diagnoses using standardized clinical vignettes: a preliminary study.International journal of emergency medicine · 2026Article
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
- Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach.Journal of clinical medicine · 2026Article
- Multimodal large language model versus emergency physicians for burn assessment: a prospective non-inferiority study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Article
- Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement.Journal of Korean biological nursing science · 2025Review
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
Artificial intelligence (AI) is increasingly improving the processes such as emergency patient care and emergency medicine education. This scoping review aims to map the use and performance of AI models in emergency medicine regarding AI concepts. The findings show that AI-based medical imaging systems provide disease detection with 85%-90% accuracy in imaging techniques such as X-ray and computed tomography scans. In addition, AI-supported triage systems were found to be successful in correctly classifying low- and high-urgency patients. In education, large language models have provided high accuracy rates in evaluating emergency medicine exams. However, there are still challenges in the integration of AI into clinical workflows and model generalization capacity. These findings demonstrate the potential of updated AI models, but larger-scale studies are still needed.
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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.