Korean J Anesthesiol.  2020 Aug;73(4):275-284. 10.4097/kja.19475.

Discovering hidden information in biosignals from patients using artificial intelligence

  • 1Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
  • 2Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea


Biosignals such as electrocardiogram or photoplethysmogram are widely used for determining and monitoring the medical condition of patients. It was recently discovered that more information could be gathered from biosignals by applying artificial intelligence (AI). At present, one of the most impactful advancements in AI is deep learning. Deep learning-based models can extract important features from raw data without feature engineering by humans, provided the amount of data is sufficient. This AI-enabled feature presents opportunities to obtain latent information that may be used as a digital biomarker for detecting or predicting a clinical outcome or event without further invasive evaluation. However, the black box model of deep learning is difficult to understand for clinicians familiar with a conventional method of analysis of biosignals. A basic knowledge of AI and machine learning is required for the clinicians to properly interpret the extracted information and to adopt it in clinical practice. This review covers the basics of AI and machine learning, and the feasibility of their application to real-life situations by clinicians in the near future.


Artificial intelligence; Biomarker; Deep learning; Computer-assisted diagnosis; Electrodiagnosis; Electrocardiography
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