Abstract
Traditional cardiovascular care relies on episodic, resource-intensive evaluations. Consumer wearable and portable devices, combined with artificial intelligence (AI), offer a scalable, low-cost alternative. These devices can enhance care with high-fidelity cardiovascular data captured outside traditional care settings, with AI further increasing their value. This review explores how AI-enhanced digital health tools can transform cardiovascular care, improving early detection, personalized risk assessment, and proactive management, particularly in resource-constrained settings, while bridging gaps in traditional care models.
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1 Yale School of Medicine, Section of Cardiovascular Medicine, Department of Internal Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Medicine, Cardiovascular Data Science (CarDS) Lab, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)
2 Yale School of Medicine, Section of Cardiovascular Medicine, Department of Internal Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Medicine, Cardiovascular Data Science (CarDS) Lab, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale-New Haven Hospital, Center for Outcomes Research and Evaluation, New Haven, USA (GRID:grid.417307.6) (ISNI:0000 0001 2291 2914); Yale School of Public Health, Section of Health Informatics, Department of Biostatistics, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710)