The study by Nzeako et al. aims to survey how artificial intelligence is being used across interventional cardiology to improve diagnosis, guide procedures, support decisions, and personalize care. The authors conducted a structured literature review across PubMed, Scopus, and Google Scholar, focusing on the past decade and including peer‑reviewed, English-language studies that apply machine learning/deep learning in interventional cardiology. The studies were screened via PRISMA and synthesized narratively with quality appraisal. For diagnostics, models were run on ECGs, echocardiography, CT/MRI, and intravascular images to boost read accuracy and cut reader-to-reader differences, while wearables/remote monitors flagged atrial fibrillation and dangerous rhythms earlier. For procedures, tools assisted angiogram assessment, catheter/device tracking, stent sizing/placement, and dynamic road‑mapping with less contrast, showing that FFR‑CT improved workflow and sharpened revascularization choices. For decisions and personalization, AI‑CDSS supported risk stratification, outcome prediction, and medication selection, and multimodal models combined clinical, imaging, and genetic data to tailor therapy and automate calcium scoring. Despite strong technical performance, broad adoption hinges on robust external validation, bias mitigation, privacy/security safeguards, regulatory clarity, workflow integration, clinician training, and infrastructure, AI should supplement, not replace, clinician judgment, with emphasis on explainability and ongoing oversight.