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 İlhan, Fatih Doğanay
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.