Role of Artificial Intelligence and Machine Learning in Interventional Cardiology

Directed by 2 decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of compu-tational power has allowed for rapid progress in artifi-cial intelligence (AI). In interventional cardiology, artificial intelligence has shown potential in providing data interpretation and automated analysis from elec-trocardiogram, echocardiography, computed tomogra-phy angiography, magnetic resonance imaging, and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in inter-ventional cardiology procedures. Robot-assisted percu-taneous coronary intervention, along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning algo-rithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of machine learning has emerged as a deep learning algorithm for numerous car-diovascular applications. However, the impact deep learning on the future of cardiology practice is not clear. Predictive models based on deep learning have several limitations including low generalizability and decision processing in cardiac anatomy. (Curr Probl Cardiol 2023;48:101698.)