Key result
AI tools boost interventional cardiology planning and risk prediction, improving post-PCI bleeding C-statistics to ~0.80.
Why the study?
Rapid advances in machine learning and deep learning have created new opportunities for automated cardiovascular dataset analysis and decision support across the coronary care continuum, motivating a synthesis of current and emerging AI applications in interventional cardiology.
This review highlights how artificial intelligence is evolving from a pre-procedural diagnostic imaging adjunct into a comprehensive intraprocedural decision-support and risk-prediction platform in interventional cardiology.
Answers are grounded in this paper and cite their sources.
AI applications may support interventional cardiology workflows; leaves open the need for prospective outcome trials before clinical adoption.
Background: AI is rapidly reshaping cardiovascular medicine, particularly within imaging-intensive fields such as interventional cardiology. Advances in machine learning and deep learning have enabled automated analysis of complex cardiovascular datasets and opened new opportunities for decision support across the coronary care continuum. Objectives: This review summarises current and emerging applications of AI in interventional cardiology, spanning pre-procedural imaging, intraprocedural guidance, outcome prediction, and future technological developments. Key findings: In the pre-procedural setting, AI-enhanced coronary CT angiography, AI-derived fractional flow reserve, and automated plaque characterisation improve risk stratification and patient selection for invasive angiography. Within the catheterisation laboratory, AI applications include automated angiographic interpretation, intravascular ultrasound and optical coherence tomography analysis, lesion classification, optimisation of stent sizing and deployment, emerging roles in structural heart interventions, and periprocedural haemodynamic monitoring. Machine learning models are also increasingly used for risk stratification, predicting bleeding, contrast-induced nephropathy, and mortality after percutaneous coronary intervention. Despite these advances, important challenges remain, including regulatory oversight, algorithm validation, bias mitigation, data governance, clinician education, and unresolved liability issues. Conclusion: AI is evolving from a diagnostic imaging adjunct to a comprehensive decision-support platform in interventional cardiology. Emerging technologies such as real-time AI-assisted catheterisation laboratory systems, multimodal data integration, digital twin modelling, and AI-guided robotic interventions may further transform coronary intervention workflows, positioning AI as a procedural co-pilot that augments rather than replaces physician expertise.
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Irannejad et al. (2026) conducted a review in Coronary artery disease and structural heart disease. Artificial intelligence in interventional cardiology vs. Conventional non-AI clinical and imaging assessment was evaluated. AI-assisted tools enhance pre-procedural imaging, intraprocedural guidance, and risk prediction in interventional cardiology, improving post-PCI bleeding C-statistics to 0.75-0.85 vs 0.65-0.75.
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