Paper
Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology
Published Dec 1, 2021 · Yisak Kim, Ji Yoon Park, Eui Jin Hwang
Journal of Thoracic Disease
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Abstract
Objective This review will focus on how AI—and, specifically, deep learning—can be applied to complement aspects of the current healthcare system. We describe how AI-based tools can augment existing clinical workflows by discussing the applications of AI to worklist prioritization and patient triage, the performance-boosting effects of AI as a second reader, and the use of AI to facilitate complex quantifications. We also introduce prominent examples of recent AI applications, such as tuberculosis screening in resource-constrained environments, the detection of lung cancer with screening CT, and the diagnosis of COVID-19. We also provide examples of prognostic predictions and new discoveries beyond existing clinical practices. Background Artificial intelligence (AI) has shown promising performance for thoracic diseases, particularly in the field of thoracic radiology. However, it has not yet been established how AI-based image analysis systems can help physicians in clinical practice. Methods This review included peer-reviewed research articles on AI in the thorax published in English between 2015 and 2021. Conclusions With advances in technology and appropriate preparation of physicians, AI could address various clinical problems that have not been solved due to a lack of clinical resources or technological limitations. Keywords Artificial intelligence (AI); deep learning (DL); computer aided diagnosis (CAD); thoracic radiology; pulmonary medicine
AI-based tools can enhance clinical workflows and address various clinical problems in thoracic radiology, potentially addressing resource and technological limitations.
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