Discovering hidden information in biosignals from patients by artificial intelligence

Biosignals like electrocardiogram or photoplethysmogram have been widely used for monitoring and determining status of patients. However, it has been recently discovered that more information than that we have used traditionally were included in the biosignals after artificial intelligence (AI) was...

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Bibliographic Details
Published in:Korean journal of anesthesiology pp. 275 - 284
Main Authors: Yoon, Dukyong, Jang, Jong-Hwan, Choi, Byung Jin, Kim, Tae Young, Han, Chang Ho
Format: Journal Article
Language:English
Published: Korea (South) 대한마취통증의학회 01.08.2020
Subjects:
마취과학
Electrocardiography
Electrodiagnosis
Biomarker
Artificial intelligence
Computer-Assisted Diagnosis
Deep Learning
ISSN:2005-6419, 2005-7563
Online Access:Get full text
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Summary:Biosignals like electrocardiogram or photoplethysmogram have been widely used for monitoring and determining status of patients. However, it has been recently discovered that more information than that we have used traditionally were included in the biosignals after artificial intelligence (AI) was applied. Most meaningful advancement of current AI was in deep learning. The deep learning-based models show the best performance in most area in current due to the distinguished characteristic that it is able to extract important features from raw data. For that, deep learning extracts features in data by itself without feature engineering by human, if amount of data is enough for that. These AI-enabled feature give us opportunities to have a chance to see novel information which was hidden for many decades. It will be able to be used as digital biomarker for detecting or for predicting clinical outcome or event without further or more invasive evaluation. However, because the characteristics of deep learning is black box model, it is difficult to understand to use if users have the traditional view on the biosignals. For properly use of the novel information which is being discovered by AI and for adopting that in real clinical practice, clinicians need to basic knowledge on the AI and machine learning. This review covers from the basis of AI and machine learning for clinicians and its feasibilities in real practice within near future.
ISSN:2005-6419
2005-7563
DOI:10.4097/kja.19475