Evidence
Can J Cardiol. 2024 Jul 9:S0828-282X(24)00523-3. doi: 10.1016/j.cjca.2024.07.003. Online ahead of print.
ABSTRACT
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, non-cardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases (CVD) in the last five years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, a majority of these studies are single-center, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for <15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration (FDA) have been developed through commercial collaborations, with about half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multi-center large datasets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in CVD management.
PMID:38992812 | DOI:10.1016/j.cjca.2024.07.003
Estimated reading time: 5 minute(s)
Latest: Psychiatryai.com #RAISR4D Evidence
Cool Evidence: Engaging Young People and Students in Real-World Evidence
Real-Time Evidence Search [Psychiatry]
AI Research
Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications
🌐 90 Days
Evidence Blueprint
Diagnostic and Prognostic Models Based on Electrocardiograms for Rapid Clinical Applications
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence