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The aim is to evaluate whether smart worklist prioritization by artificialintelligence (AI) can optimize the radiology workflow and reduce reportturnaround times (RTAT) for critical findings in chest radiographs (CXRs).Furthermore, we investigate a method to counteract the effect of false negativepredictions by AI -- resulting in an extremely and dangerously long RTAT, asCXRs are sorted to the end of the worklist.  We developed a simulation framework that models the current workflow at auniversity hospital by incorporating hospital specific CXR generation rates,reporting rates and pathology distribution. Using this, we simulated thestandard worklist processing "first-in, first-out" (FIFO) and compared it witha worklist prioritization based on urgency. Examination prioritization wasperformed by the AI, classifying eight different pathological findings rankedin descending order of urgency: pneumothorax, pleural effusion, infiltrate,congestion, atelectasis, cardiomegaly, mass and foreign object. Furthermore, weintroduced an upper limit for the maximum waiting time, after which the highesturgency is assigned to the examination.  The average RTAT for all critical findings was significantly reduced in allPrioritization-simulations compared to the FIFO-simulation (e.g. pneumothorax:35.6 min vs. 80.1 min; p $<0.0001$), while the maximum RTAT for most findingsincreased at the same time (e.g. pneumothorax: 1293 min vs 890 min; p$<0.0001$). Our "upper limit" substantially reduced the maximum RTAT allclasses (e.g. pneumothorax: 979 min vs. 1293 min / 1178 min; p $<0.0001$).  Our simulations demonstrate that smart worklist prioritization by AI canreduce the average RTAT for critical findings in CXRs while maintaining a smallmaximum RTAT as FIFO.

Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation