ReviewKorean journal of anesthesiology2020
Discovering hidden information in biosignals from patients using artificial intelligence.
Review in Korean journal of anesthesiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Work at Heights Training: Conventional Approach with and Without Immersive Virtual Reality Study Protocol.Methods and protocols · 2026Article
- Electrocardiogram-based deep learning score for coronary artery calcification reclassifies cardiovascular risk and identifies screening candidates.BMC medical informatics and decision making · 2026Article
- Machine Learning-Based Identification of Patients with Elevated Central Venous Pressure Using Features Extracted from Photoplethysmography Waveforms.medRxiv : the preprint server for health sciences · 2025Article
- Artificial Intelligence-Based Electrocardiogram Model as a Predictor of Postoperative Atrial Fibrillation Following Cardiac Surgery: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- Learning Multidimensional Electronegativity of Selected Atom Types in Organic Molecules Using Graph Neural Networks.ACS omega · 2025Article
- Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.Biomedical engineering letters · 2025Review
- Artificial Intelligence-Based Early Prediction of Acute Respiratory Failure in the Emergency Department Using Biosignal and Clinical Data.Yonsei medical journal · 2025Article
- Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.Sensors (Basel, Switzerland) · 2024Review
- Machine learning with clinical and intraoperative biosignal data for predicting postoperative delirium after cardiac surgery.iScience · 2024Article
- An Explainable Artificial Intelligence-enabled ECG Framework for the Prediction of Subclinical Coronary Atherosclerosis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Artificial intelligence in perioperative medicine: a narrative review.Korean journal of anesthesiology · 2022Review
- Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia.Frontiers in neuroinformatics · 2022Article
- Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success.International journal of environmental research and public health · 2021Article
- Applications of deep learning methods in digital biomarker research using noninvasive sensing data.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.