Artificial Intelligence for Cardiovascular Care in Action

Abstract

Artificial intelligence (AI) is catalyzing a new era in cardiovascular care. Innovations include the detection of latent signatures enabling early diagnosis, real-time nudges for optimized clinical decision-making, and digital twin simulations to personalize treatment. The integration of AI in health systems has the potential to improve patient outcomes and enhance the efficiency of care delivery across diverse populations. However, many challenges remain-including interoperability and data privacy, algorithmic fairness and sustainability, and system considerations such as workflow integration and policy. Here, we draw examples from state-of-the-art AI applications to examine the interplay between AI and learning health systems to augment continuous measurement and feedback, and implementation science to robustly evaluate the efficacy and safety of AI interventions deployed into clinical workflows. We share a vision for "AI in action", transitioning cardiovascular health systems from sparse, reactive, and hospital-centric episodes to a multimodal, connected ecosystem continuously learning to improve patient outcomes.

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