Retrospective comparison of a deep learning algorithm for diagnosing chest radiograph abnormalities, indicating potential for improving patient access.
In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.
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Rajpurkar et al. (2018) studied this question.
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