The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.
Introduction: Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical fram...
| Publicado en: | Archives of Academic Emergency Medicine Vol. 11; no. 1; pp. 1 - 29 |
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| Autores principales: | , , , , |
| Formato: | algorithm pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Shahid Beheshti University of Medical Sciences
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174770051&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174770051 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 26454904 MFMK jtl: Archives of Academic Emergency Medicine issn: 26454904 maglogo: N pubinfo: dt: 2023 vid: 11 iid: 1 pid: 87963 pub: Shahid Beheshti University of Medical Sciences artinfo: ui: 174770051 174770051 174770051 10.22037/aaem.v11i1.1974 174770051 ppf: 1 ppct: 28 formats: fmt: @attributes: type: P tig: atl: The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. aug: au: Hosseini, Mohsen Masoumian Hosseini, Seyedeh Toktam Masoumian Qayumi, Karim Ahmady, Soleiman Koohestani, Hamid Reza affil: Department of E-learning in Medical Sciences, SMART University of Medical Sciences, Tehran, Iran sug: subj: Artificial Intelligence Utilization Artificial Intelligence Ethical Issues Emergency Care Evaluation Emergency Medicine Human Scoping Review Internet PubMed Medline Emergency Service Thematic Analysis Machine Learning Prehospital Care Triage Descriptive Statistics Decision Making Neural Networks (Computer) Social Media Prediction Models Outcomes (Health Care) ab: Introduction: Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use. Methods: A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted. Results: A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%). Conclusion: There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque. pubtype: Academic Journal doctype: algorithm pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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