AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review.

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Cardiovascular diseases are a leading global cause of death, necessitating improved detection and prediction methods.
• Wearable devices offer noninvasive, continuous monitoring of cardiovascular health, but generate large data volumes requiring advanced AI for real-time analysis.
• Effective clinical decision-making relies on real-time analysis of cardiovascular signals from wearable devices.

Purpose of the Study:

• To identify challenges in AI-driven platforms for real-time cardiovascular monitoring using wearable devices.
• To explore potential solutions and examine AI algorithm development for robust monitoring.
• To investigate optimization of deployment pipelines for real-time cardiovascular monitoring.

Main Methods:

• A comprehensive literature search was conducted across six major electronic databases.
• Inclusion criteria focused on studies using wearable devices and AI for real-time cardiovascular event detection or prediction.
• A total of 19 studies met the inclusion criteria after rigorous screening and full-text review.

Main Results:

• Research on AI-driven real-time cardiovascular monitoring with wearables is limited and lacks comprehensive validation.
• Most studies utilized public datasets, with few real-world community-based validations.
• Electrocardiography wearables were common, often in hospital settings, with AI deployed on-device or via cloud.

Conclusions:

• Interdisciplinary research is essential to realize the potential of AI-driven real-time cardiovascular health management.
• Scalable solutions for continuous, community-based deployment require development and validation.
• Real-world challenges including participant compliance, hardware/connectivity, and AI model optimization need careful consideration.