Combining Wearable Sensors and Artificial Intelligence in Cardiovascular Medicine

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, stimulating ongoing research into innovative approaches for their prevention, diagnosis, and management. Sensor technologies play a crucial role in this scenario, enabling accurate and continuous monitoring of cardiac-related electrical, mechanical, and pulsatile activities both in clinical environments and in everyday life. Advances in wearable sensors, along with novel measurement techniques, have led to the availability of large volumes of high-resolution physiological data. Such rich data stream has, in turn, catalyzed the integration of artificial intelligence (AI) methodologies into cardiovascular medicine. This article provides a concise overview of the current landscape of wearable technologies for capturing heart-related signals that are critical for the detection and management of CVDs. Particular attention is given to challenges inherent in measuring reliable physiological data in real-world conditions. Beyond discussing sensing hardware, the review emphasizes the interplay with advanced AI approaches, illustrating how techniques, such as generative AI, explainable AI, federated learning, reinforcement learning, cross-domain adaptation, multimodal AI, and edge AI can help extract meaningful insights from complex, multidimensional sensor data, enhancing decision-making and enabling personalized care. By combining advances in sensors, measurement science, and AI, this survey highlights the potential of wearable systems to support earlier diagnosis, enable continuous disease management, and ultimately improve clinical outcomes, while also discussing limitations of the current state of the art.