Artificial intelligence in interventional cardiology: a review of its role in diagnosis, decision-making, and procedural precision

Abstract

Cardiovascular diseases significantly burden healthcare systems globally, necessitating innovative solutions to enhance diagnosis, treatment, and patient management. Artificial intelligence (AI) is no longer a distant promise in interventional cardiology but a rapidly emerging tool with growing clinical impact. AI-driven technologies can analyze vast amounts of clinical data, recognize intricate patterns, and generate clinically relevant, evidence-based recommendations, augmenting physician expertise and streamlining care. In diagnostics, AI enhances imaging interpretation and lesion assessment, while procedurally, it supports real-time guidance and catheter-based interventions. Its integration into decision support systems has improved risk stratification, early disease detection, and individualized treatment planning. AI also advances personalized medicine using predictive models to tailor interventions to patient-specific needs. Despite its promise, challenges such as costs, ethical issues, and the need for rigorous validation remain barriers to widespread adoption. Nevertheless, as AI advances, its integration into interventional cardiology is expected to transform care delivery, optimize outcomes, and improve system efficiency.

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