Harnessing Artificial Intelligence for Precision Cardiovascular Medicine

Abstract

Artificial intelligence (AI) has revolutionized cardiology diagnostic capabilities by improving precision, effectiveness, and prompt identification of various cardiac diseases. AI subfields include machine learning, deep learning, and cognitive computing. Machine learning can be supervised, unsupervised, or reinforcement learning. Support vector machines (SVM), deep learning, and artificial neural networks (ANN) are commonly used in the medical field for handling large and complex data. ANNs perform better than SVMs in evaluating electrocardiogram (ECG) data, while SVMs are used for disease stratification. AI-driven diagnostic tools have transformed the interpretation of ECGs, echocardiograms, cardiac imaging, and other diagnostic modalities, leading to better patient outcomes and more accurate clinical decision-making. AI has shown promise in identifying and describing coronary artery disease, with machine learning models training on cardiac CT images. Non-invasive AI tools, like HeartFlow, help patients with cardiac autonomic dysfunction (CAD) with treatment planning and decision-making. AI systems often require access to sensitive patient data, raising concerns about privacy, data security, and consent. Additionally, using patient data without clear ethical oversight may erode public trust. Transparency in how AI tools use and protect data is essential to maintain ethical standards in cardiovascular medicine.

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