  Objectives: To assess the use of artificial intelligence-based software in
ruling out chest X-ray cases, with no significant findings in a primary health
care setting.
  Methods: In this retrospective study, a commercially available artificial
intelligence (AI) software was used to analyse 10 000 chest X-rays of Finnish
primary health care patients. In studies with a mismatch between an AI normal
report and the original radiologist report, a consensus read by two
board-certified radiologists was conducted to make the final diagnosis.
  Results: After the exclusion of cases not meeting the study criteria, 9579
cases were analysed by AI. Of these cases, 4451 were considered normal in the
original radiologist report and 4644 after the consensus reading. The number of
cases correctly found nonsignificant by AI was 1692 (17.7% of all studies and
36.4% of studies with no significant findings). After the consensus read, there
were nine confirmed false-negative studies. These studies included four cases
of slightly enlarged heart size, four cases of slightly increased pulmonary
opacification and one case with a small unilateral pleural effusion. This gives
the AI a sensitivity of 99.8% (95% CI= 99.65-99.92) and specificity of 36.4 %
(95% CI= 35.05-37.84) for recognising significant pathology on a chest X-ray.
  Conclusions: AI was able to correctly rule out 36.4% of chest X-rays with no
significant findings of primary health care patients, with a minimal number of
false negatives that would lead to effectively no compromise on patient safety.
No critical findings were missed by the software.
