The deployment of multiple AI-triage devices in radiology departments has grown rapidly, yet the cumulative impact on patient wait-times across different disease conditions remains poorly understood. This research develops a comprehensive mathematical and simulation framework to quantify wait-time trade-offs when multiple AI-triage devices operate simultaneously in a clinical workflow. We created multi-QuCAD, a software tool that models complex multi-AI, multi-disease scenarios using queueing theory principles, incorporating realistic clinical parameters including disease prevalence rates, radiologist reading times, and AI performance characteristics from FDA-cleared devices. The framework was verified through four experimental scenarios ranging from simple two-disease workflows to complex nine-disease systems, comparing preemptive versus non-preemptive scheduling disciplines and priority versus hierarchical triage protocols. Analysis of brain imaging workflows demonstrated that while AI-triage devices significantly reduce wait-times for target conditions, they can substantially delay diagnosis of non-targeted, yet urgent conditions. The study revealed that hierarchical protocol generally provides more wait-time savings for the highest-priority conditions compared to the priority protocol, though at the expense of more delays to lower-priority patients with other time-sensitive conditions. The quantitative framework presented provides essential insights for orchestrating multi-AI deployments to maximize overall patient time-saving benefits while minimizing unintended delay for other important patient populations.

翻译：放射科中多个AI分诊设备的部署已迅速增长，但不同疾病条件下对患者等待时间的累积影响仍知之甚少。本研究开发了一个全面的数学与仿真框架，用于量化临床工作流中多个AI分诊设备同时运行时的等待时间权衡。我们创建了multi-QuCAD软件工具，该工具基于排队论原理对复杂的多AI、多疾病场景进行建模，整合了包括疾病患病率、放射科医师阅片时间以及FDA批准设备的AI性能特征在内的真实临床参数。该框架通过四个实验场景进行了验证，涵盖从简单的双疾病工作流到复杂的九疾病系统，比较了抢占式与非抢占式调度策略以及优先级与分层分诊协议。对脑影像工作流的分析表明，尽管AI分诊设备显著降低了目标疾病的等待时间，但可能大幅延迟非目标但紧急疾病的诊断。研究发现，与优先级协议相比，分层协议通常能为最高优先级疾病提供更多的等待时间节省，但代价是其他具有时间敏感性的低优先级患者面临更多延迟。本研究提出的量化框架为协调多AI部署提供了关键见解，旨在最大化整体患者的时间节省效益，同时最小化对其他重要患者群体的意外延误。