Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges

Research output: Contribution to journalReview article

Abstract

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.

Original languageEnglish
Pages (from-to)511-525
Number of pages15
JournalKorean Journal of Radiology
Volume21
Issue number5
DOIs
StatePublished - 1 May 2020

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Keywords

  • Artificial intelligence
  • Chest radiograph
  • Chest X-ray
  • Computed tomography
  • Deep learning

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