Key result
AI and deep neural networks enhance cardiac telemetry by improving accuracy and detecting complex patterns.
Why the study?
AI has increasingly integrated into cardiac telemetry, shifting from traditional machine learning to advanced deep neural networks to enhance real-time monitoring and personalized care.
This review highlights the transition from traditional machine learning to deep neural networks in cardiac telemetry, emphasizing their potential to enhance real-time monitoring and personalized cardiac care.
Highlights the potential of deep learning to personalize real-time cardiac monitoring; leaves open clinical.
Cardiac telemetry has evolved into a vital tool for continuous cardiac monitoring and early detection of cardiac abnormalities. In recent years, artificial intelligence (AI) has become increasingly integrated into cardiac telemetry, making a shift from traditional statistical machine learning models to more advanced deep neural networks. These modern AI models have demonstrated superior accuracy and the ability to detect complex patterns in telemetry data, enhancing real-time monitoring, predictive analytics and personalised cardiac care. In our review, we examine the current state of AI in cardiac telemetry, focusing on deep learning techniques, their clinical applications, the challenges and limitations faced by these models, and potential future directions in this promising field.
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Lu et al. (2025) conducted a review in Cardiac abnormalities. Artificial intelligence was evaluated. Artificial intelligence and deep neural networks enhance cardiac telemetry by improving accuracy and detecting complex patterns for real-time monitoring and predictive analytics.
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