RT Journal Article
SR Electronic
T1 Building artificial intelligence and machine learning models : a primer for emergency physicians
JF Emergency Medicine Journal
JO Emerg Med J
FD BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine
SP e1
OP e1
DO 10.1136/emermed-2022-212379
VO 39
IS 5
A1 Ramlakhan, Shammi L
A1 Saatchi, Reza
A1 Sabir, Lisa
A1 Ventour, Dale
A1 Shobayo, Olamilekan
A1 Hughes, Ruby
A1 Singh, Yardesh
YR 2022
UL http://emj.bmj.com/content/39/5/e1.abstract
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.