  Interpretation of chest computed tomography (CT) is time-consuming. Previous
studies have measured the time-saving effect of using a deep-learning-based aid
(DLA) for CT interpretation. We evaluated the joint impact of a multi-pathology
DLA on the time and accuracy of radiologists' reading.
  40 radiologists were randomly split into three experimental arms: control
(10), who interpret studies without assistance; informed group (10), who were
briefed about DLA pathologies, but performed readings without it; and the
experimental group (20), who interpreted half studies with DLA, and half
without. Every arm used the same 200 CT studies retrospectively collected from
BIMCV-COVID19 dataset; each radiologist provided readings for 20 CT studies. We
compared interpretation time, and accuracy of participants diagnostic report
with respect to 12 pathological findings.
  Mean reading time per study was 15.6 minutes [SD 8.5] in the control arm,
13.2 minutes [SD 8.7] in the informed arm, 14.4 [SD 10.3] in the experimental
arm without DLA, and 11.4 minutes [SD 7.8] in the experimental arm with DLA.
Mean sensitivity and specificity were 41.5 [SD 30.4], 86.8 [SD 28.3] in the
control arm; 53.5 [SD 22.7], 92.3 [SD 9.4] in the informed non-assisted arm;
63.2 [SD 16.4], 92.3 [SD 8.2] in the experimental arm without DLA; and 91.6 [SD
7.2], 89.9 [SD 6.0] in the experimental arm with DLA. DLA speed up
interpretation time per study by 2.9 minutes (CI95 [1.7, 4.3], p<0.0005),
increased sensitivity by 28.4 (CI95 [23.4, 33.4], p<0.0005), and decreased
specificity by 2.4 (CI95 [0.6, 4.3], p=0.13).
  Of 20 radiologists in the experimental arm, 16 have improved reading time and
sensitivity, two improved their time with a marginal drop in sensitivity, and
two participants improved sensitivity with increased time. Overall, DLA
introduction decreased reading time by 20.6%.
