February
Tuesday, February 24, 2026
8:00am-9:00am PT
Watch Recording Here
David Kim, MD, PhD
Professor of Emergency Medicine
Stanford University
Title: AI-Driven Imaging Queue Prioritization in the Emergency Department
Abstract: Delays in emergency department (ED) imaging can compromise patient safety, yet queues typically operate first-in, first-out (FIFO) regardless of clinical urgency. While AI has improved post-imaging interpretation speeds, the critical “pre-acquisition” waiting period remains unoptimized. We developed a discrete-event simulation of 131,027 CT studies, calibrated to real-world operations, to evaluate AI-driven queue prioritization. A LightGBM model predicted clinical actionability at the time of order (AUROC 0.766), allowing dynamic re-ordering of the queue. In a held-out evaluation (n=20,795), the AI policy reduced median wait times for actionable studies by 10.8 minutes, increasing the proportion of actionable findings obtained within one hour from 48.3% to 57.3%. Crucially, the system effectively targeted the dangerous “tail” of the distribution: 90th percentile wait times for actionable patients dropped by 43.3 minutes (196.2 to 152.9 minutes). This acceleration came with minimal impact on lower-acuity patients. Our approach captures 80-87% of the theoretical maximum benefit, suggesting that pre-acquisition AI triage can significantly reduce time-to-diagnosis for the sickest patients without requiring additional hardware.
I am an NIH-funded emergency physician and researcher specializing in the application of artificial intelligence and patient monitoring to improve emergency care delivery. My research program focuses on developing and validating multimodal AI systems that integrate physiologic signals with clinical context to predict patient trajectories and support decision-making.