Purpose To evaluate the diagnostic performance of a commercial artificial intelligence (AI) solution in detecting actionable abnormalities (AAs) on chest radiographs (CRs) and to compare its performance with that of thoracic radiologists.Materials and Methods Among 986,016 CRs performed at a single academic hospital between 2016 and 2021, 194 CRs were retrospectively identified as actionable based on critical value reports.Age-and sex-matched 388 normal CRs were selected as controls.A commercial AI solution capable of detecting 10 thoracic abnormalities was retrospectively applied to all CRs.Three thoracic radiologists independently reviewed the same dataset.Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive rate, precision, F1 score, detection yield, and false-referral rate (FRR).Interobserver agreement and agreement between the AI and the radiologists were also assessed. ResultsThe AI solution demonstrated excellent diagnostic performance, with an AUC of 0.958, a sensitivity of 90.7%, and a specificity of 91.2%.Compared with the radiologists, the
Kim et al. (Thu,) studied this question.
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