Artificial intelligence in emergency radiology: A review of applications and possibilities

Artificial intelligence (AI) applications in radiology have been rising exponentially in the last decade. Although AI has found usage in various areas of healthcare, its utilization in the emergency department (ED) as a tool for emergency radiologists shows great promise towards easing some of the challenges faced daily. There have been numerous reported studies examining the application of AI-based algorithms in identifying common ED conditions to ensure more rapid reporting and in turn quicker patient care. In addition to inter-pretive applications, AI assists with many of the non-interpretive tasks that are encountered every day by emergency radiologists. These include, but are not limited to, protocolling, image quality control and work-flow prioritization. AI continues to face challenges such as physician uptake or costs, but is a long-term investment that shows great potential to relieve many difficulties faced by emergency radiologists and ulti-mately improve patient outcomes. This review sums up the current advances of AI in emergency radiology, including current diagnostic applications (interpretive) and applications that stretch beyond imaging (non-interpretive), analyzes current drawbacks of AI in emergency radiology and discusses future challenges.(c) 2022 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Automated summary

The utilization of artificial intelligence (AI) in radiology, particularly in the emergency department (ED), has been growing exponentially in the past decade. AI-based algorithms have been studied extensively and show promise in identifying common ED conditions for faster reporting and better patient care. In addition to interpretive applications, AI also helps with non-interpretive tasks such as protocoling and workflow prioritization. Although there are challenges to overcome, AI has the potential to alleviate difficulties faced by emergency radiologists and ultimately improve patient outcomes in the long term.