Building artificial intelligence and machine learning models : a primer for emergency physicians. Issue 5 (3rd March 2022)

• Record Type: Journal Article
• Authors: Ramlakhan, Shammi L; Saatchi, Reza; Sabir, Lisa; Ventour, Dale; Shobayo, Olamilekan; Hughes, Ruby; Singh, Yardesh
• Is Part Of: Emergency medicine journal. Volume 39:Issue 5(2022)
• Page Start: e1
• Page End: e1
• Publication Date: 2022-03-03
• Subjects: methods -- research design -- statistics; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2022-212379
• Languages: English
• ISSNs: 1472-0205

Abstract

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.