Archives of Academic Emergency Medicine
Artificial Intelligence as a Second Reader for Focused Point-of-Care Ul-trasound (POCUS) in the Emergency Department: A Scoping Review:
By Milad Bakhshi, Saba Aghajanai and Aliraza Majidi.
Abstract
Introduction
Artificial intelligence (AI)/ machine learning (ML) as a second reader for focused point-of-care ultrasound (POCUS) may address two key emergency department (ED) challenges: operator variability and workflow delays. This review maps the existing literature on the application of AI/ML as a second reader for POCUS in the ED.
Methods
We included studies on AI/ML for POCUS interpretation, acquisition guidance, or quality assessment in the ED/urgent care settings, with AI as a second reader supporting clinical decisions. We excluded fully autonomous AI, non-emergency settings, healthy volunteers, phantom data, and non-English publications. Two reviewers independently screened records from five databases (PubMed/MEDLINE, Scopus, Embase, Web of Science, IEEE Xplore; 2020– August 17 2026), extracting data on study characteristics, POCUS application, AI role, performance metrics, technology readiness level (TRL), and clinical implementation status.