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Author Credentials

Katherine Konczak MD candidate; Elizabeth G Dieter BS candidate; Robert S Dieter II BS, MS; Robert S Dieter MD, RVT, ALM; Aravinda Nanjundappa MD

Keywords

artificial intelligence, cardiovascular medicine, electrophysiology, machine learning, data sets, cardiac imaging

Disciplines

Medicine and Health Sciences

Abstract

Artificial intelligence (AI), particularly machine learning (ML), is increasingly transforming cardiovascular medicine by enhancing diagnostic accuracy, imaging analysis, risk prediction, and treatment planning. AI applications in imaging, such as echocardiography, cardiac computed tomography (CT), and magnetic resonance imaging (MRI), improve detection, automate measurements, and reduce variability. Deep learning models interpret electrocardiograms (ECGs) with high accuracy in electrophysiology, aiding early diagnosis of arrhythmias and inherited syndromes. In cardio-oncology, AI identifies cardiovascular risk markers in cancer patients and monitors therapy-related cardiotoxicity. AI-driven tools also support real-time procedural guidance and individualized therapy using electronic health records and imaging data. Despite promising outcomes, concerns about generalizability, data security, and algorithmic fairness remain. Nonetheless, AI represents a natural evolution in cardiovascular care, offering a path toward more precise, efficient, and patient-centered medicine.

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