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Review
. 2025 Jul 7;17(7):e87494.
doi: 10.7759/cureus.87494. eCollection 2025 Jul.

Harnessing Artificial Intelligence in Interventional Cardiology: A Systematic Review of Current Applications

Priyansh Patel  1   2 , Besiki Davitashvili  3 , Sai Sujana Chitturi  4 , Mari Gadaevi  3 , Diya Patel  5 , Jayanth Reddy Tallapalli  6 , Jyothsna Butchireddy  7 , Ria Suresh  8 , Jayanth Jhishnu Nannegari  9 , Shivam Slathia  10
Affiliations
Review

Harnessing Artificial Intelligence in Interventional Cardiology: A Systematic Review of Current Applications

Priyansh Patel et al. Cureus. .

Abstract

Interventional cardiology has recently advanced with innovations such as percutaneous transluminal coronary angioplasty (PTCA), transcatheter aortic valve replacement (TAVR), and the emergence of artificial intelligence (AI) as a transformative tool. This systematic review explored the current landscape, methodologies, and applications of AI in interventional cardiology. A comprehensive literature search was conducted following preferred reporting guidelines, identifying 20 studies after data extraction and quality assessment. AI-particularly machine learning (ML) and deep learning (DL)-enhances diagnostic accuracy and procedural efficiency. ML aids in arrhythmia detection and coronary plaque characterization, while DL supports imaging interpretation, robotic navigation, and catheter tracking. Clinical applications show AI's potential in predicting myocardial infarction, guiding personalized treatment, and improving resource management. Despite these benefits, challenges such as data privacy, algorithm transparency, and generalizability remain. Addressing these requires collaborative efforts and robust data sharing. Future priorities include integrating AI into routine clinical workflows, resolving regulatory barriers, and ensuring interpretability. Multidisciplinary collaboration is essential to address ethical considerations and uphold patient safety. The integration of AI in interventional cardiology offers significant potential to enhance patient care, procedural precision, and resource utilization. However, its adoption must be guided by careful attention to ethical, technical, and regulatory constraints. Overcoming these barriers through coordinated efforts may allow AI to redefine standards in cardiovascular care and enable a more precise, efficient, and patient-centered approach to interventional cardiology.

Keywords: ai & robotics healthcare; ai and machine learning; artificial intelligence (ai); cognitive computing; convolutional neural networks (cnn); deep learning (dl); interventional cardiology; natural language processing (nlp).

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Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. PRISMA flow diagram
PRISMA: Preferred reporting items for systematic reviews and meta-analyses
See this image and copyright information in PMC

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