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Building artificial intelligence and machine learning models : a primer for emergency physicians
  1. Shammi L Ramlakhan1,
  2. Reza Saatchi2,
  3. Lisa Sabir1,
  4. Dale Ventour3,
  5. Olamilekan Shobayo2,
  6. Ruby Hughes4,
  7. Yardesh Singh3
  1. 1 Emergency Department, Sheffield Children's NHS Foundation Trust, Sheffield, UK
  2. 2 Electronics & Computer Engineering Research Institute, Sheffield Hallam University, Sheffield, UK
  3. 3 Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago
  4. 4 Advanced Forming Research Centre, University of Strathclyde, Sheffield, UK
  1. Correspondence to Dr Shammi L Ramlakhan, Emergency Department, Sheffield Children's NHS Foundation Trust, Sheffield S10 2TH, UK; sramlakhan{at}


There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emergency department (ED) implementations being developed. For EM researchers building models de novo, collaborative working with data scientists is invaluable throughout the process. Synergism and understanding between domain (EM) and data experts increases the likelihood of realising a successful real-world model. Our linked manuscript provided a conceptual framework (including a glossary of AI terms) to support clinicians in interpreting AI research. The aim of this paper is to supplement that framework by exploring the key issues for clinicians and researchers to consider in the process of developing an AI model.

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  • Handling editor Katie Walker

  • Twitter @shammi_ram

  • Contributors SLR—conceptualisation and writing (original draft). All other authors—writing and editing.

  • 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.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

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

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