AI-Enabled Triage and Workflow Integration in Emergency and Trauma Imaging

The field of emergency radiology is witnessing a rapid transformation as artificial intelligence (AI) tools become integral to the detection and prioritization of life-threatening findings across trauma and acute care imaging. Recent advances have seen AI-driven software deployed for intracranial hemorrhage (ICH) detection on CT, stroke large vessel occlusion (LVO) triage on CTA/CT perfusion, and identification of pulmonary embolism or aortic catastrophes—each area demonstrating improved sensitivity, reduced time to diagnosis, and potential for workflow optimization. However, despite promising results in controlled environments, real-world evidence for effective triage, safety, and human–AI integration in busy emergency departments remains limited, particularly regarding external validation, calibration under dataset shift, and human factors affecting adoption. Ongoing debates focus on the necessity of linking technical AI performance to clinical outcomes, the challenges of multi-site validation, and ensuring equity and reliability across diverse populations and imaging platforms.This Research Topic aims to consolidate robust clinical and translational evidence for the use of AI in the rapid triage and prioritization of time-critical findings in emergency radiology. The goal is to advance knowledge on how AI algorithms can measurably reduce report turnaround times, improve the prioritization of conditions such as intracranial hemorrhage, stroke, pulmonary embolism, aortic dissection, and pneumothorax, and support safer, more accurate decision-making across emergency and trauma settings. Key objectives include examining external validation across sites and vendors, benchmarking performance against clinical outcomes, evaluating workflow integration and human factors, and assessing the real-world impact on diagnostic safety and patient management.To gather further insights in this rapidly evolving field, we welcome articles focused on original research, pragmatic trials, external validation studies, and systematic reviews limited to clinically actionable findings within emergency imaging. We encourage submissions addressing, but not limited to, the following themes:• ICH detection and prioritization on head CT• LVO/stroke triage from CTA/CTP• Pulmonary embolism detection/prioritization on CTPA• Aortic dissection and rupture screening on CTA• Pneumothorax/hemothorax on CXR/CT• Worklist integration and human factors in ED settings• Calibration, failure modes, and uncertainty reporting Topic editors: Gagandeep Singh, Gianluca De Rubeis Source: https://www.frontiersin.org/research-topics/82791/ai-enabled-triage-and-workflow-integration-in-emergency-and-trauma-imaging