  The aim is to evaluate whether smart worklist prioritization by artificial
intelligence (AI) can optimize the radiology workflow and reduce report
turnaround times (RTAT) for critical findings in chest radiographs (CXRs).
Furthermore, we investigate a method to counteract the effect of false negative
predictions by AI -- resulting in an extremely and dangerously long RTAT, as
CXRs are sorted to the end of the worklist.
  We developed a simulation framework that models the current workflow at a
university hospital by incorporating hospital specific CXR generation rates,
reporting rates and pathology distribution. Using this, we simulated the
standard worklist processing "first-in, first-out" (FIFO) and compared it with
a worklist prioritization based on urgency. Examination prioritization was
performed by the AI, classifying eight different pathological findings ranked
in descending order of urgency: pneumothorax, pleural effusion, infiltrate,
congestion, atelectasis, cardiomegaly, mass and foreign object. Furthermore, we
introduced an upper limit for the maximum waiting time, after which the highest
urgency is assigned to the examination.
  The average RTAT for all critical findings was significantly reduced in all
Prioritization-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 findings
increased at the same time (e.g. pneumothorax: 1293 min vs 890 min; p
$<0.0001$). Our "upper limit" substantially reduced the maximum RTAT all
classes (e.g. pneumothorax: 979 min vs. 1293 min / 1178 min; p $<0.0001$).
  Our simulations demonstrate that smart worklist prioritization by AI can
reduce the average RTAT for critical findings in CXRs while maintaining a small
maximum RTAT as FIFO.
