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Objectives: To assess the use of artificial intelligence-based software inruling out chest X-ray cases, with no significant findings in a primary healthcare setting.  Methods: In this retrospective study, a commercially available artificialintelligence (AI) software was used to analyse 10 000 chest X-rays of Finnishprimary health care patients. In studies with a mismatch between an AI normalreport and the original radiologist report, a consensus read by twoboard-certified radiologists was conducted to make the final diagnosis.  Results: After the exclusion of cases not meeting the study criteria, 9579cases were analysed by AI. Of these cases, 4451 were considered normal in theoriginal radiologist report and 4644 after the consensus reading. The number ofcases correctly found nonsignificant by AI was 1692 (17.7% of all studies and36.4% of studies with no significant findings). After the consensus read, therewere nine confirmed false-negative studies. These studies included four casesof slightly enlarged heart size, four cases of slightly increased pulmonaryopacification and one case with a small unilateral pleural effusion. This givesthe 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 nosignificant findings of primary health care patients, with a minimal number offalse negatives that would lead to effectively no compromise on patient safety.No critical findings were missed by the software.

Using artificial intelligence to detect chest X-rays with no significant findings in a primary health care setting in Oulu, Finland