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Archives of Pharmacal Research

, Volume 42, Issue 6, pp 492–504 | Cite as

Applications of deep learning for the analysis of medical data

  • Hyun-Jong Jang
  • Kyung-Ok ChoEmail author
Review

Abstract

Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other machine learning methods. To understand how deep learning has surpassed traditional machine learning techniques, in this review, we briefly explore the basic learning algorithms underlying deep learning. In addition, the procedures for building deep learning-based classifiers for seizure electroencephalograms and gastric tissue slides are described as examples to demonstrate the simplicity and effectiveness of deep learning applications. Finally, we review the clinical applications of deep learning in radiology, pathology, and drug discovery, where deep learning has been actively adopted. Considering the great advantages of deep learning techniques, deep learning will be increasingly and widely utilized in a wide variety of different areas in medicine in the coming decades.

Keywords

Artificial intelligence Deep neural networks Drug discovery Medical image analysis 

Notes

Acknowledgements

This work was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare (HI15C2854).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© The Pharmaceutical Society of Korea 2019

Authors and Affiliations

  1. 1.Department of Physiology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, College of MedicineThe Catholic University of KoreaSeoulSouth Korea
  2. 2.Department of Pharmacology, Department of Biomedicine & Health Sciences, Catholic Neuroscience Institute, Institute of Aging and Metabolic Diseases, College of MedicineThe Catholic University of KoreaSeoulSouth Korea

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