  Objectives: The present study evaluated the impact of a commercially
available explainable AI algorithm in augmenting the ability of clinicians to
identify lung cancer on chest X-rays (CXR).
  Design: This retrospective study evaluated the performance of 11 clinicians
for detecting lung cancer from chest radiographs, with and without assistance
from a commercially available AI algorithm (red dot, Behold.ai) that predicts
suspected lung cancer from CXRs. Clinician performance was evaluated against
clinically confirmed diagnoses.
  Setting: The study analysed anonymised patient data from an NHS hospital; the
dataset consisted of 400 chest radiographs from adult patients (18 years and
above) who had a CXR performed in 2020, with corresponding clinical text
reports.
  Participants: A panel of readers consisting of 11 clinicians (consultant
radiologists, radiologist trainees and reporting radiographers) participated in
this study.
  Main outcome measures: Overall accuracy, sensitivity, specificity and
precision for detecting lung cancer on CXRs by clinicians, with and without AI
input. Agreement rates between clinicians and performance standard deviation
were also evaluated, with and without AI input.
  Results: The use of the AI algorithm by clinicians led to an improved overall
performance for lung tumour detection, achieving an overall increase of 17.4%
of lung cancers being identified on CXRs which would have otherwise been
missed, an overall increase in detection of smaller tumours, a 24% and 13%
increased detection of stage 1 and stage 2 lung cancers respectively, and
standardisation of clinician performance.
  Conclusions: This study showed great promise in the clinical utility of AI
algorithms in improving early lung cancer diagnosis and promoting health equity
through overall improvement in reader performances, without impacting
downstream imaging resources.
