Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education DOI Creative Commons
Göksu Bozdereli Berikol, Altuğ Kanbakan, Buğra İlhan

et al.

Turkish Journal of Emergency Medicine, Journal Year: 2025, Volume and Issue: 25(2), P. 67 - 91

Published: April 1, 2025

Artificial intelligence (AI) is increasingly improving the processes such as emergency patient care and medicine education. This scoping review aims to map use performance of AI models in regarding concepts. The findings show that AI-based medical imaging systems provide disease detection with 85%-90% accuracy techniques X-ray computed tomography scans. In addition, AI-supported triage were found be successful correctly classifying low- high-urgency patients. education, large language have provided high rates evaluating exams. However, there are still challenges integration into clinical workflows model generalization capacity. These demonstrate potential updated models, but larger-scale studies needed.

Language: Английский

Bring a ‘Patient’s Medical AI Journey’ to the Hill DOI Creative Commons
Ian Stevens,

Erin Williams,

Jean‐Christophe Bélisle‐Pipon

et al.

The American Journal of Bioethics, Journal Year: 2025, Volume and Issue: 25(3), P. 132 - 135

Published: Feb. 24, 2025

Language: Английский

Citations

0
Predicting triage of pediatric patients in the emergency department using machine learning approach DOI Creative Commons
Manal Ahmed Halwani, Ghada Merdad, Miada Almasre

et al.

International Journal of Emergency Medicine, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 10, 2025

Abstract Background The efficient performance of an Emergency Department (ED) relies heavily on effective triage system that prioritizes patients based the severity their medical conditions. Traditional systems, including those using Canadian Triage and Acuity Scale (CTAS), may involve subjective assessments by healthcare providers, leading to potential inconsistencies delays in patient care. Objective This study aimed evaluate six Machine Learning (ML) models K-Nearest Neighbors (KNN), Support Vector (SCM), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), Light GBM (Light Gradient Boosting Machine) for prediction King Abdulaziz University Hospital CTAS framework. Methodology We followed three essential phases: data collection (7125 records ED patients), exploration processing, development machine learning predictive at Hospital. Results conclusion overall was highest GNB = 0.984 accuracy. CTAS-level model indicated SVM, RF, LGBM achieved regarding consistency precision recall values across all levels.

Language: Английский

Citations

0
Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education DOI Creative Commons
Göksu Bozdereli Berikol, Altuğ Kanbakan, Buğra İlhan

et al.

Turkish Journal of Emergency Medicine, Journal Year: 2025, Volume and Issue: 25(2), P. 67 - 91

Published: April 1, 2025

Artificial intelligence (AI) is increasingly improving the processes such as emergency patient care and medicine education. This scoping review aims to map use performance of AI models in regarding concepts. The findings show that AI-based medical imaging systems provide disease detection with 85%-90% accuracy techniques X-ray computed tomography scans. In addition, AI-supported triage were found be successful correctly classifying low- high-urgency patients. education, large language have provided high rates evaluating exams. However, there are still challenges integration into clinical workflows model generalization capacity. These demonstrate potential updated models, but larger-scale studies needed.

Language: Английский

Citations

0