Artificial intelligence in interventional cardiology: present landscape and future directions
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Abstract
With population aging and the rising incidence of cardiac disease, interventional cardiology, as a cornerstone of treatment for conditions such as coronary and structural heart disease, faces persistent challenges, including stringent precision requirements, heavy reliance on operator experience, and variability in diagnostic and therapeutic efficiency. With robust capabilities in data mining, image recognition, real-time analysis, and predictive modeling, artificial intelligence (AI) offers a novel pathway to address the aforementioned challenges and support the intelligent transformation of interventional cardiology. This narrative review briefly outlines the background to the application of AI in interventional cardiology, focuses on core supporting technologies including deep learning, machine learning, and computer vision, and summarizes current research and clinical applications of AI across preprocedural risk assessment and planning, intraprocedural real-time navigation and early warning of complications, and postprocedural follow-up and prognosis prediction. It also analyzes the principal challenges currently facing AI in this field, such as data quality and security issues, insufficient algorithm interpretability, barriers to clinical translation, and constraints from ethical and regulatory frameworks. Finally, in light of technological advances and clinical needs, this review outlines future directions, including the integration of AI with interventional devices, multimodal data integration and modeling, and the development of personalized interventional strategies, thereby providing a reference for promoting the standardized and large-scale application of AI technology in interventional cardiology.
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Li J, Li Y, Zhu Q, Liu X. Artificial intelligence in interventional cardiology: present landscape and future directions. Vessel Plus. 2026;10:56. https://dx.doi.org/10.20517/2574-1209.2026.12
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