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Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology

Cited 13 time in Web of Science Cited 15 time in Scopus

Kim, Yisak; Park, Ji Yoon; Hwang, Eui Jin; Lee, Sang Min; Park, Chang Min

Issue Date
Pioneer Bioscience Publishing Company (PBPC)
Journal of Thoracic Disease, Vol.13 No.12, pp.6943-6962
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 resourceconstrained 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.
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