Artificial intelligence in interventional cardiology: current applications and future clinical integration

Abstract

Artificial intelligence (AI) has been revolutionizing invasive cardiology in recent years, with respect to diagnostic accuracy, procedural success, and long-term patient outcomes. Advanced machine learning (ML) and deep learning algorithms facilitate automated image analysis, risk stratification, and personalized intervention planning, paving the way for precision medicine. AI-based technologies, such as coronary computed tomography angiography, intravascular ultrasound, and optical coherence tomography, enable precise plaque definition and quantification that goes beyond classical subjective methods. AI-based quantitative computed tomography and radiomics-based approaches have demonstrated strong correlations with invasive standards such as NIRS-IVUS, effectively identifying lipid-rich plaques and predicting acute coronary events. AI is also refining risk stratification models, significantly improving predictive capabilities compared to traditional methods, thus enabling personalized therapeutic interventions in real time. In interventional cardiology, the integration of real-time AI with fluoroscopy significantly improves procedural decision making while reducing procedure time, radiation exposure, and operator variability. Additionally, AI-assisted predictive analytics facilitate comprehensive risk assessment, optimizing treatment strategies by accurately identifying patients at the highest risk of major adverse cardiovascular events. ML algorithms improve image analysis by automating plaque characterization, thus facilitating clinical decision making and procedural optimization. In the future, AI-based applications, such as AI-guided catheter navigation, could further transform PCI, opening new possibilities for innovation and optimization. Despite these advances, challenges remain regarding data standardization, algorithmic interpretability, regulatory compliance, and ethical concerns about data privacy and potential bias. This review will explore the risks and potential benefits of this unprecedented evolution.

Keywords

Artificial intelligence / machine learning / deep learning / AI-assisted coronary computed tomography angiography / coronary artery disease / detection / computational hemodynamics

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