Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education

Göksu Bozdereli Berikol; Altuğ Kanbakan; Buğra Ilhan; Fatih Doğanay

doi:10.4103/tjem.tjem_45_25

Göksu Bozdereli Berikol¹, Altuğ Kanbakan¹, Buğra Ilhan², Fatih Doğanay³

¹Department of Emergency Medicine, Ufuk University School of Medicine, Ankara, Türkiye
²Department of Emergency Medicine, Kırıkkale University School of Medicine, Kırıkkale, Türkiye
³Department of Emergency Medicine, University of Health Sciences School of Medicine, İstanbul, Türkiye

Keywords: Artificial intelligence, emergency medicine, image processing, large language models, machine learning, signal processing

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.

How to cite this article: Berikol GB, Kanbakan A, Ilhan B, Doğanay F. Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education. Turk J Emerg Med 2025;25:67-91.