Artificial intelligence and machine learning in emergency medicine: a narrative review

• Published in: Acute medicine & surgery Vol. 9; no. 1; pp. e740 - n/a
• Main Authors: Mueller, Brianna, Kinoshita, Takahiro, Peebles, Alexander, Graber, Mark A., Lee, Sangil
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.01.2022; John Wiley and Sons Inc; Wiley
• Subjects: Artificial intelligence; Body mass index; deep learning; Emergency medical care; emergency medicine; machine learning; Neural networks; prediction; Review; Reviews; prediction; deep learning; machine learning; emergency medicine; Artificial intelligence
• ISSN: 2052-8817; 2052-8817
• DOI: 10.1002/ams2.740

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. This article covers a basic concept of machine learning in emergency medicine.