PT  - JOURNAL ARTICLE
AU  - Ramlakhan, Shammi L
AU  - Saatchi, Reza
AU  - Sabir, Lisa
AU  - Ventour, Dale
AU  - Shobayo, Olamilekan
AU  - Hughes, Ruby
AU  - Singh, Yardesh
TI  - Building artificial intelligence and machine learning models : a primer for emergency physicians
AID  - 10.1136/emermed-2022-212379
DP  - 2022 May 01
TA  - Emergency Medicine Journal
PG  - e1--e1
VI  - 39
IP  - 5
4099  - http://emj.bmj.com/content/39/5/e1.short
4100  - http://emj.bmj.com/content/39/5/e1.full
SO  - Emerg Med J2022 May 01; 39
AB  - 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.