Why the study?
Arrhythmia detection and cardiovascular health monitoring are rapidly changing due to wearable technology, prompting an examination of established and new sensor technologies integrated with artificial intelligence.
Does the integration of artificial intelligence in wearable electronics improve the real-time detection and management of cardiac arrhythmias?
Does the integration of artificial intelligence in wearable electronics improve the real-time detection and management of cardiac arrhythmias?
AI-enhanced wearable electronics offer a promising, non-invasive approach for continuous, real-time arrhythmia detection, achieving high diagnostic accuracy and facilitating early clinical intervention.
May support opportunistic AI-wearable AF screening to enable earlier anticoagulation; confirms prior diagnostic meta-analyses and extends RCT evidence on treatment uptake.
The field of arrhythmia detection and cardiovascular health monitoring is rapidly changing due to wearable technology. With a focus on developments in flexible materials, sensor integration, and electronic design for ongoing arrhythmia monitoring, this review offers a thorough examination of both established and new wearable sensor technologies. The article describes both commercial and experimental devices, such as textile-based, patch, and wrist-worn platforms, emphasizing their performance in clinical and real-world settings as well as their sensing modalities, including bioelectrical, optoelectrical, and mechanoelectrical techniques. The integration of machine learning and artificial intelligence (AI) algorithms is given special attention because it greatly improves wearable monitors' clinical utility, predictive power, and diagnostic accuracy. We conducted this systematic review of case studies, focusing on the use of deep learning to analyze photoplethysmography and electrocardiogram data, and their impact on earlier detection and improved management of atrial fibrillation and other arrhythmias. We performed a systemic review analyzing 58 studies for the period of 2018-2025 over the issues of data security, regulatory approval, signal fidelity, user adherence, and sensor ergonomics. In order to enhance long-term wearability and user comfort, the review also looks at the market environment, legal frameworks, and advancements in material science, including textile-integrated graphene electrodes and epidermal electronics. The importance of interoperable device architectures, strong privacy protection that complies with international standards, and the ongoing development of AI-driven analytics for real-time decision support in healthcare are highlighted as future research directions. The purpose of the synthesis is to direct researchers, engineers, and clinicians toward the upcoming generation of patient-centered, intelligent wearable technologies for arrhythmia detection.
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Sharma et al. (2025) studied this question.
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