• Acad Emerg Med · Feb 2025

    Artificial intelligence-based clinical decision support in the emergency department: A scoping review.

    • Hashim Kareemi, Krishan Yadav, Courtney Price, Niklas Bobrovitz, Andrew Meehan, Henry Li, Gautam Goel, Sameer Masood, Lars Grant, Maxim Ben-Yakov, Wojtek Michalowski, and Christian Vaillancourt.
    • Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
    • Acad Emerg Med. 2025 Feb 4.

    ObjectiveArtificial intelligence (AI)-based clinical decision support (CDS) has the potential to augment high-stakes clinical decisions in the emergency department (ED). However, its current usage and translation to implementation remains poorly understood. We asked: (1) What is the current landscape of AI-CDS for individual patient care in the ED? and (2) What phases of development have AI-CDS tools achieved?MethodsWe performed a scoping review of AI for prognostic, diagnostic, and treatment decisions regarding individual ED patient care. We searched five databases (MEDLINE, EMBASE, Cochrane Central, Scopus, Web of Science) and gray literature sources from January 1, 2010, to December 11, 2023. We adhered to guidelines from the Joanna Briggs Institute and PRISMA Extension for Scoping Reviews. We published our protocol on Open Science Framework (DOI 10.17605/OSF.IO/FDZ3Y).ResultsOf 5168 unique records identified, we selected 605 studies for inclusion. The majority (369, 61%) were published in 2021-2023. The studies ranged over a variety of clinical applications, patient populations, and AI model types. Prognostic outcomes were most commonly assessed (270, 44.6%), followed by diagnostic (193, 31.9%) and disposition (115, 19%). Most studies remained in the earliest phase of preclinical development (572, 94.5%) with few advancing to later phases (33, 5.5%).ConclusionsBy thoroughly mapping the landscape of AI-CDS in the ED, we demonstrate a rapidly increasing volume of studies covering a breadth of clinical applications, yet few have achieved advanced phases of testing or implementation. A more granular understanding of the barriers and facilitators to implementing AI-CDS in the ED is needed.© 2025 Society for Academic Emergency Medicine.

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