Building artificial intelligence and machine learning models : a primer for emergency physicians

RAMLAKHAN, Shammi, SAATCHI, Reza, SABIR, Lisa, VENTOUR, Dale, HUGHES, Ruby, SHOBAYO, Olamilekan and SINGH, Yardesh (2022). Building artificial intelligence and machine learning models : a primer for emergency physicians. Emergency Medical Journal.

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Official URL: https://emj.bmj.com/content/early/2022/03/02/emerm...
Link to published version:: https://doi.org/10.1136/emermed-2022-212379
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    Abstract

    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.

    Item Type: Article
    Identification Number: https://doi.org/10.1136/emermed-2022-212379
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 15 Feb 2022 13:22
    Last Modified: 11 Mar 2022 12:15
    URI: http://shura.shu.ac.uk/id/eprint/29746

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