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Artificial intelligence and machine learning in emergency medicine: a narrative review.
Mueller, Brianna; Kinoshita, Takahiro; Peebles, Alexander; Graber, Mark A; Lee, Sangil.
Afiliación
  • Mueller B; Department of Business Analytics The University of Iowa Tippie College of Business Iowa City Iowa USA.
  • Kinoshita T; Philips Research North America Cambridge Massachusetts USA.
  • Peebles A; Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA.
  • Graber MA; Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA.
  • Lee S; Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA.
Acute Med Surg ; 9(1): e740, 2022.
Article en En | MEDLINE | ID: mdl-35251669
AIM: The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine. METHODS: We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments. RESULTS: This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI. CONCLUSION: We intend that this review serves as an introduction to AI and machine learning in emergency medicine.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Acute Med Surg Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Acute Med Surg Año: 2022 Tipo del documento: Article