van de Leur, RR;
Boonstra, MJ;
Bagheri, A;
Roudijk, RW;
Sammani, A;
Taha, K;
Doevendans, PAFM;
... Asselbergs, FW; + view all
(2020)
Big data and artificial intelligence: Opportunities and threats in electrophysiology.
Arrhythmia and Electrophysiology Review
, 9
(3)
pp. 146-154.
10.15420/AER.2020.26.
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Abstract
The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.
| Type: | Article |
|---|---|
| Title: | Big data and artificial intelligence: Opportunities and threats in electrophysiology |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.15420/AER.2020.26 |
| Publisher version: | https://doi.org/10.15420/AER.2020.26 |
| Language: | English |
| Additional information: | This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for noncommercial purposes, provided the original work is cited correctly. https://creativecommons.org/licenses/by-nc/4.0/legalcode |
| Keywords: | Artificial intelligence, deep learning, neural networks, cardiology, electrophysiology, ECG, big data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10119405 |
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