Simulation of AI-driven CT Queue Prioritization in the Emergency Department.

Purpose To evaluate the effect of an artificial intelligence (AI)-driven CT queue prioritization system on emergency department CT wait times using a discrete-event simulation. Materials and Methods Multimodal data from 313 966 emergency department visits (August 2020-August 2024) were retrospectively analyzed. A gradient boosting machine model was trained to predict the clinical actionability of CT studies at the time of order. Discrete-event simulations, calibrated to real-world operations, were conducted to compare a first-in, first-out policy against an AI-driven prioritization policy on the internal test set of 20 795 studies (March-August 2024). Wait times for actionable and nonactionable studies were compared between the first-in, first-out and AI-based dispatch policies, with bootstrap 95% CIs, and ceiling analyses using perfect predictions. Results Compared with the first-in, first-out policy, the AI-based dispatch policy reduced median wait times for actionable studies in the internal test set (7637 of 20 795 [36.73%]) by 10.75 minutes (95% CI: -12.40, -9.10) and 90th-percentile wait times by 43.36 minutes (95% CI: -50.58, -36.80). The proportion of actionable findings obtained within 1 hour increased from 48.33% (3691 of 7637) to 57.30% (4376 of 7637). For nonactionable studies, the median wait time was reduced by 5.80 minutes (95% CI: -7.00, -4.40), but the 90th-percentile wait time increased by 14.46 minutes (95% CI: 6.40, 23.20). The model captured 80%-87% of the maximum benefit achievable with perfect predictions. Conclusion AI-driven CT queue prioritization can help reduce the time to diagnosis for critical findings with minimal disruption to lower-acuity patients, using existing data streams and without the need for additional hardware. Keywords: Artificial Intelligence, CT, Emergency Department, Discrete-Event Simulation, Workflow, Queue Prioritization Supplemental material is available for this article. © RSNA, 2026 See also the editorial by Pfeiffer and Thalhammer in this issue.