Artificial Intelligence and Advanced Computational Tools in Percutaneous Coronary Intervention: A Contemporary Critical Appraisal

The term artificial intelligence (AI) is increasingly applied to a broad spectrum of digital technologies in interventional cardiology, encompassing machine learning (ML), deep learning (DL), computational modelling, advanced imaging analytics, robotics, and extended reality systems.In the setting of percutaneous coronary intervention (PCI), these technologies have primarily demonstrated value in enhancing image interpretation, procedural planning, workflow efficiency, and operator support.However, despite rapid technological adoption, robust randomized evidence demonstrating improvements in hard clinical outcomes remains limited.This review provides a critical appraisal of AI and AI-adjacent computational technologies relevant to PCI, with particular emphasis on clarifying conceptual distinctions, current clinical capabilities, and evidentiary limitations.We distinguish true data-driven ML and DL applications from physics-based simulations, robotics, and visualization platforms, and evaluate their respective roles across the PCI continuum.Upstream applications in coronary artery disease risk assessment and noninvasive imaging are discussed only when they directly inform invasive procedural decision-making.Downstream technologies, including angiography-based physiology, intravascular imaging analytics, computational stent simulation, robot-assisted PCI, and augmented or virtual reality guidance, are examined with a focus on procedural optimization rather than proven outcome modification.While these technologies consistently improve diagnostic efficiency, procedural precision, and operator ergonomics, claims of outcome superiority should be interpreted cautiously.This review highlights key knowledge gaps, regulatory and ethical challenges, and the need for prospective trials with clinically meaningful endpoints.Rather than replacing interventionalists, digital intelligence should be viewed as a decision-support ecosystem that augments human expertise within the catheterization laboratory.