1. Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology
- Author
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van de Leur, R.R., Boonstra, M.J., Bagheri, A., Roudijk, R.W., Sammani, A., Taha, K., Doevendans, P.A.F.M., van der Harst, P., van Dam, P.M., Hassink, R.J., van Es, R., Asselbergs, F.W., Methodology and statistics for the behavioural and social sciences, and Leerstoel Heijden
- Subjects
Big data ,030204 cardiovascular system & hematology ,Field (computer science) ,Diagnostic modalities ,03 medical and health sciences ,0302 clinical medicine ,big data ,Physiology (medical) ,Medicine ,Diseases of the circulatory (Cardiovascular) system ,030212 general & internal medicine ,Artificial neural network ,business.industry ,ECG ,Deep learning ,Clinical performance ,deep learning ,artificial intelligence ,neural networks ,electrophysiology ,cardiology ,RC666-701 ,Clinical Arrhythmias ,Narrative review ,Artificial intelligence ,Applications of artificial intelligence ,Cardiology and Cardiovascular Medicine ,business - 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.
- Published
- 2020