Journal of thoracic disease · 2021
Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology.
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Kim Yisak, Park Ji Yoon, Hwang Eui Jin, Lee Sang Min, Park Chang Min
Аннотация
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.
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.