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Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine
  1. Shammi Ramlakhan1,
  2. Reza Saatchi2,
  3. Lisa Sabir1,
  4. Yardesh Singh3,
  5. Ruby Hughes4,
  6. Olamilekan Shobayo2,
  7. Dale Ventour3
  1. 1Emergency Department, Sheffield Children's Hospital, Sheffield, UK
  2. 2Electronics and Computer Engineering Research Institute, Sheffield Hallam University, Sheffield, UK
  3. 3Department of Clinical Surgical Sciences, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
  4. 4Simulation and Modelling Unit, Advanced Forming Research Centre, University of Strathclyde, Sheffield, UK
  1. Correspondence to Dr Shammi Ramlakhan, Emergency Department, Sheffield Children's Hospital, Sheffield, UK; sramlakhan{at}nhs.net

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Introduction

The field of artificial intelligence (AI) has been developing more prominently for over half a century. Innovations in computer processing power and analytical capabilities coupled with the availability of huge amounts of routinely collected data has meant that AI research and technology development has grown exponentially in recent years. The results of this growth can be seen in emergency medicine (EM)—with the Food and Drug Administration approving the first AI software as a medical device for wrist fracture detection in 2018. As of 2021, several more have been approved—for triage, X-ray identification of pneumothorax and notification and triage software for CT images.1

Between 2015 and 2021, there were over 500 publications indexed in MEDLINE involving AI in acute and emergency care, with more than half of these published within the last 2 years alone. There is recognition that AI technology can potentially play an important role in ED decision making, workflow and operations.2–4 However, concerns with unstructured and often opaque reporting, inappropriate algorithm selection, proxy bias, data privacy and safety have led to calls for better standards for undertaking and reporting of research involving AI.5–9 For practising ED clinicians, this will facilitate interpretation and understanding of AI research prior to model deployment or generalisation.

The aim of this paper is to serve as a primer for clinicians and researchers in understanding common AI methods as they relate to EM, and to provide a framework for interpreting AI research. A companion paper provides a more detailed exploration of the AI model building pipeline in an EM context.

What is the promise of AI for EM?

AI technology is seen in multiple aspects of day-to-day living—from email spam filters, voice activated devices, suggestions from entertainment streaming services and social media to self-driving cars—all are powered by AI of varying complexities. The natural extension into healthcare, and EM …

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Footnotes

  • Handling editor Katie Walker

  • Twitter @shammi_ram

  • Contributors Conceptualisation, writing original draft: SLR. Writing—review and editing: all other authors.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests SLR is a Decision Editor for the EMJ.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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