HOCUS POCUS - Exploring the use of Artificial Intelligence in Point of Care Ultrasound Application in the Emergency Department

Abstract

The field of emergency medicine has changed since the first introduction of portable ultrasound fifty years ago. Smaller equipment and the ability to produce higher quality images has driven the wide adoption of Point-of-Care Ultrasound (PoCUS) in emergency departments. PoCUS is an integral tool for physicians to obtain images in real-time and rapidly diagnose critically ill patients for timely intervention when every minute matters. Recognizing Core applications of PoCUS has been highlighted by the Canadian Association of Emergency Physicians and training programs are within the core curriculum for EM residency. However, the main challenge of PoCUS is that the diagnostic accuracy and interpretability is dependent on operator expertise. With the resurgent interest in artificial intelligence (AI) in healthcare, its integration into PoCUS was promising. Several studies have looked at integrating AI analysis with PoCUS imaging to improve diagnostic accuracy, guide training models, and increase the accessibility of PoCUS for novice users. Given that PoCUS usage improves patient satisfaction and clinician confidence, increased PoCUS usage is a worthy and achievable goal in Canadian emergency departments. The promising adaptation of artificial intelligence with PoCUS assessments will serve to expand training and diagnostic confidence to improve patient outcomes.