Göksu Bozdereli Berikol, Altuğ Kanbakan, Buğra Ilhan, Fatih Doğanay
{"title":"Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.","authors":"Göksu Bozdereli Berikol, Altuğ Kanbakan, Buğra Ilhan, Fatih Doğanay","doi":"10.4103/tjem.tjem_45_25","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":46536,"journal":{"name":"Turkish Journal of Emergency Medicine","volume":"25 2","pages":"67-91"},"PeriodicalIF":1.1000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12002153/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Turkish Journal of Emergency Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/tjem.tjem_45_25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EMERGENCY MEDICINE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Turkish Journal of Emergency Medicine (Turk J Emerg Med) is an International, peer-reviewed, open-access journal that publishes clinical and experimental trials, case reports, invited reviews, case images, letters to the Editor, and interesting research conducted in all fields of Emergency Medicine. The Journal is the official scientific publication of the Emergency Medicine Association of Turkey (EMAT) and is printed four times a year, in January, April, July and October. The language of the journal is English. The Journal is based on independent and unbiased double-blinded peer-reviewed principles. Only unpublished papers that are not under review for publication elsewhere can be submitted. The authors are responsible for the scientific content of the material to be published. The Turkish Journal of Emergency Medicine reserves the right to request any research materials on which the paper is based. The Editorial Board of the Turkish Journal of Emergency Medicine and the Publisher adheres to the principles of the International Council of Medical Journal Editors, the World Association of Medical Editors, the Council of Science Editors, the Committee on Publication Ethics, the US National Library of Medicine, the US Office of Research Integrity, the European Association of Science Editors, and the International Society of Managing and Technical Editors.