Artificial intelligence and machine learning in emergency medicine: a narrative review
Brianna Mueller, Takahiro Kinoshita, Alexander T. Peebles, Mark A Graber, Sangil Lee
Abstract
AIM: The emergence and evolution of artificial intelligence (AI) has generated increasing interest in machine learning applications for health care. Specifically, researchers are grasping the potential of machine learning solutions to enhance the quality of care in emergency medicine. METHODS: We undertook a narrative review of published works on machine learning applications in emergency medicine and provide a synopsis of recent developments. RESULTS: This review describes fundamental concepts of machine learning and presents clinical applications for triage, risk stratification specific to disease, medical imaging, and emergency department operations. Additionally, we consider how machine learning models could contribute to the improvement of causal inference in medicine, and to conclude, we discuss barriers to safe implementation of AI. CONCLUSION: We intend that this review serves as an introduction to AI and machine learning in emergency medicine.