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Applications of Artificial Intelligence in Helicopter Emergency Medical Services: A Scoping Review.
Hsueh, Jennifer; Fritz, Christie; Thomas, Caroline E; Reimer, Andrew P; Reisner, Andrew T; Schoenfeld, David; Haimovich, Adrian; Thomas, Stephen H.
Afiliación
  • Hsueh J; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA. Electronic address: jhsueh@bidmc.harvard.edu.
  • Fritz C; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
  • Thomas CE; Georgetown University, Washington, DC.
  • Reimer AP; Case Western Reserve University Frances Payne Bolton School of Nursing, Cleveland, OH; Cleveland Clinic Critical Care Transport, Cleveland, OH.
  • Reisner AT; Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
  • Schoenfeld D; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
  • Haimovich A; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
  • Thomas SH; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA; Blizard Institute, Barts and The London School of Medicine, London, United Kingdom.
Air Med J ; 43(2): 90-95, 2024.
Article en En | MEDLINE | ID: mdl-38490791
ABSTRACT

OBJECTIVE:

Recent systematic reviews of acute care medicine applications of artificial intelligence (AI) have focused on hospital and general prehospital uses. The purpose of this scoping review was to identify and describe the literature on AI use with a focus on applications in helicopter emergency medical services (HEMS).

METHODS:

A literature search was performed with specific inclusion and exclusion criteria. Articles were grouped by characteristics such as publication year and general subject matter with categoric and temporal trend analyses.

RESULTS:

We identified 21 records focused on the use of AI in HEMS. These applications included both clinical and triage uses and nonclinical uses. The earliest study appeared in 2006, but over one third of the identified studies have been published in 2021 or later. The passage of time has seen an increased likelihood of HEMS AI studies focusing on nonclinical issues; for each year, the likelihood of a nonclinical focus had an odds ratio of 1.3.

CONCLUSION:

This scoping review provides overview and hypothesis-generating information regarding AI applications specific to HEMS. HEMS AI may be ultimately deployed in nonclinical arenas as much as or more than for clinical decision support. Future studies will inform future decisions as to how AI may improve HEMS systems design, asset deployment, and clinical care.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Ambulancias Aéreas / Servicios Médicos de Urgencia Límite: Humans Idioma: En Revista: Air Med J Asunto de la revista: MEDICINA AEROESPACIAL / MEDICINA DE EMERGENCIA Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Ambulancias Aéreas / Servicios Médicos de Urgencia Límite: Humans Idioma: En Revista: Air Med J Asunto de la revista: MEDICINA AEROESPACIAL / MEDICINA DE EMERGENCIA Año: 2024 Tipo del documento: Article