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

•Artificial intelligence is a key to increase efficiency of emergency radiologists.•Many algorithms have been developed to detect common acute findings on imaging seen in the emergency departments.•Artificial intelligence shows promise towards rapidly identifying serious, potentially life-threatenin...

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Bibliographic Details
Published in:Diagnostic and interventional imaging 2023-01, Vol.104 (1), p.6-10
Main Authors: Katzman, Benjamin D., van der Pol, Christian B., Soyer, Philippe, Patlas, Michael N.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Emergency
Humans
Imaging
Patient outcome
Radiography
Radiologists
Radiology
Radiology - methods
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Summary:•Artificial intelligence is a key to increase efficiency of emergency radiologists.•Many algorithms have been developed to detect common acute findings on imaging seen in the emergency departments.•Artificial intelligence shows promise towards rapidly identifying serious, potentially life-threatening conditions.•AI can quickly alert the emergency radiologist to abnormal findings so that patients can receive prompt care and inevitably better outcomes. 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 interpretive 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 workflow 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 ultimately 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.
ISSN:2211-5684
2211-5684
DOI:10.1016/j.diii.2022.07.005