Applications of Artificial Intelligence and Machine Learning in Emergency Medicine Triage - A Systematic Review.

Journal: Medical archives (Sarajevo, Bosnia and Herzegovina)
PMID: 39944197

Abstract

BACKGROUND: Overcrowding in Emergency departments adversely impacts efficiency, patient outcomes, and resource allocation. Accurate triage systems are essential for prioritizing care and optimizing resources. While traditional methods provide a foundation, they often lack precision in addressing modern healthcare complexities. Artificial intelligence (AI) and machine learning (ML) offer advanced capabilities to enhance triage accuracy, improve patient prioritization, and support clinical decision-making, addressing limitations of conventional approaches and paving the way for adaptive triage solutions.

Authors

  • Qasem Ahmed Almulihi
    ER Department, King Fahad University Hospital, Al Khobar, Saudi Arabia.
  • Abdulaziz Adel Alquraini
    ER Department, MOH, Saudi Arabia.
  • Fatimah Ahmed Ali Almulihi
    ER Department, MOH, Saudi Arabia.
  • Abdullah Abdulaziz Alzahid
    ER Department, King Fahad University Hospital, Al Khobar, Saudi Arabia.
  • Saleh Saeed Al Jathnan Al Qahtani
    Teaching Assistant, Faculty of Medicine, Najran University, Saudi Arabia.
  • Mohamed Almulhim
    ER Department, King Fahad University Hospital, Al Khobar, Saudi Arabia.
  • Saeed Hussain Saeed Alqhtani
    Emergency Department, King Fahad Military Medical Complex, Dhahran.
  • Faisal Mohammed Nafea Alnafea
    Emergency Department, King Fahad Military Medical Complex, Dhahran.
  • Saad Ali Saad Mushni
    Emergency Department, King Fahad Military Medical Complex, Dhahran.
  • Nasser Abdullah Alaqil
    Family Medicine Urgent Care Department. Alkharj Armed Forces Hospital. Kharj City, Saudi Arabia.
  • Mohammad Ibrahim Faya Assiri
    ER Department, King Fahad University Hospital, Al Khobar, Saudi Arabia.
  • Nisreen H Maghraby
    ER Department, King Fahad University Hospital, Al Khobar, Saudi Arabia.

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