Utilization of Machine Learning Technologies in Emergency Department Settings: A Comprehensive Review - Takeaways - MDSpire

Utilization of Machine Learning Technologies in Emergency Department Settings: A Comprehensive Review

  • By

  • Banafshe Hosseini

  • Atushi Patel

  • Megan Landes

  • Samuel Vaillancourt

  • Muhammad Mamdani

  • Kevin Maruthananth

  • Neha Matharu

  • Zuha Pathan

  • Krishihan Sivapragasam

  • Onlak Ruangsomboon

  • Becky Skidmore

  • Andrew D Pinto

  • January 1, 2026

  • 0 min

Share

  • 1

    Emergency departments face significant challenges, including overcrowding and prolonged wait times, exacerbated by the COVID-19 pandemic.

  • 2

    Machine learning technologies offer a potential improvement in clinical prediction models, enhancing diagnostic accuracy and patient management in emergency settings.

  • 3

    This systematic review aims to evaluate the implementation of machine learning in emergency departments, focusing on clinical and operational outcomes.

  • 4

    Eligible studies included those evaluating machine learning models for predicting clinical outcomes and operational efficiencies in emergency department settings.

  • 5

    The review will summarize evidence on machine learning's impact on mortality prediction, treatment decisions, wait times, and associated costs in emergency care.

Original Source(s)

Utilization of Machine Learning Technologies in Emergency Department Settings: A Comprehensive Review

  • by Banafshe Hosseini, Atushi Patel, Megan Landes, Samuel Vaillancourt, Muhammad Mamdani, Kevin Maruthananth, Neha Matharu, Zuha Pathan, Krishihan Sivapragasam, Onlak Ruangsomboon, Becky Skidmore, Andrew D Pinto

  • January 1, 2026

Related Content

Alcohol intoxication and lack of helmet use are common in electric scooter-related traumatic brain injuries: a consecutive patient series from a tertiary university hospital

Why Hip Fracture Surgery Timing Matters

Researchers examined how variation in time to hip fracture surgery relates to mortality, complications, length of stay, and functional recovery in older adults.

Who Decides When a Doctor Steps Back?

Cognitive decline, credentialing, and why “I’ll know” often isn’t enough.