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  • 1From the Department of Radiology, Herlev and Gentofte Hospital, Borgmester Ib, Juuls vej 1 Herlev, Copenhagen 2730, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., L.C.L., M.B.A.); Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark (L.L.P., M.W.B., C.H.K., M.B., M.B.A.); Radiological Artificial Intelligence Testcenter, RAIT.dk, Herlev, Denmark (L.L.P., F.C.M., M.W.B., C.H.K., M.B., M.B.A.); Department of Radiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (M.W.B., M.B.); Department of Radiology, Aarhus University Hospital, Aarhus, Denmark (F.R.); and Department of Cardiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark (O.W.N.).

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Summary
This summary is machine-generated.

Artificial intelligence (AI) can correctly exclude pathology in unremarkable chest radiographs with 24.5%-52.7% specificity at 98% sensitivity. This AI tool demonstrated equal or lower critical miss rates compared to radiology reports at high sensitivities.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Radiology departments process a high volume of chest radiographs.
  • Artificial intelligence (AI) offers potential workflow improvements through automated reporting.
  • Evaluating AI's ability to identify unremarkable cases is crucial for its clinical integration.

Purpose of the Study:

  • To determine the specificity of an AI tool in correctly excluding pathology in unremarkable chest radiographs.
  • To assess if AI can improve workflow without increasing diagnostic errors.
  • To compare AI performance against human radiologists in identifying critical findings.

Main Methods:

  • Retrospective analysis of 1961 adult chest radiographs from four Danish hospitals.
  • Two blinded thoracic radiologists established a reference standard for 'remarkable' vs. 'unremarkable' findings.
  • A commercial AI tool's 'remarkableness' probability was used to calculate specificity at various sensitivities.

Main Results:

  • The reference standard identified 37.2% of chest radiographs as unremarkable.
  • AI achieved specificities of 24.5% to 52.7% at sensitivities of 98% to 99.9%.
  • At 95.4% sensitivity, AI had ≤1.1% critical misses, comparable to radiology reports.

Conclusions:

  • A commercial AI tool can exclude pathology in 24.5%-52.7% of unremarkable chest radiographs at ≥98% sensitivity.
  • AI demonstrated comparable or lower critical miss rates than radiology reports at high sensitivities (≥95.4%).
  • Prospective studies are recommended to validate these findings for clinical application.