Integration of AI hardware in cardiovascular clinician education

Abstract

As a cardiologist and a cardiovascular surgeon who have worked together in harmony, we have had the opportunity to witness firsthand the rapid advancements in artificial intelligence (AI). Today, AI-powered tools play a crucial role in areas such as cardiac imaging, electrophysiology, risk prediction, and decision support systems. However, the lack of formal training on AI hardware in traditional medical education presents a significant gap. Modern cardiovascular care has become highly dependent on high-performance computing systems. Technologies such as GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), FPGAs (Field-Programmable Gate Arrays), and ASICs (Application-Specific Integrated Circuits) accelerate AI applications in echocardiography analysis, ECG interpretation, and personalized treatment planning. Additionally, IoT (internet of things)-enabled wearable devices facilitate continuous patient monitoring, generating real-time data processed by AI models (1-3). While these technologies are increasingly integrated into daily practice, many clinicians remain unfamiliar with the hardware infrastructure that enables them.

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