Building an Intelligent Cardiovascular System Platform: Embedding Artificial Intelligence across All Facets of Cardiovascular Medicine

Abstract

Artificial intelligence (AI) is increasingly shaping modern cardiology by enhancing clinical interpretation through data‐driven insights, surpassing traditional subjective assessments. This review explores AI's impact on the cardiac system, emphasizing the development of an intelligent end‐to‐end platform for prediction, diagnosis, treatment, and rehabilitation. AI enables a unified ecosystem for diagnosing and treating cardiovascular (CV) diseases, both before and after symptom onset, ensuring seamless workflow integration. Key technological advances such as deep learning, federated learning, natural language processing, and multimodal data convergence form the backbone of this collaborative CV ecosystem. AI has the potential to revolutionize CV risk evaluation, personalize treatments, and enable real‐time monitoring. However, challenges remain, including improving algorithm robustness, model reliability, and safeguarding patient privacy. The review also discusses the future role of generative models, edge AI, and federated learning to improve scalability while maintaining privacy. Ultimately, AI aims to shift cardiology toward a more data‐driven, personalized, and efficient system, enhancing both patient experience and care affordability.

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