Artificial intelligence in interventional cardiology: from procedural planning to intelligent cath lab ecosystems
www.oaepublish.com
Aug. 5, 2026, 1:05 p.m.
Artificial intelligence is being progressively integrated across interventional cardiology, from pre-procedural planning through post-procedural monitoring and clinical decision support. The maturity of evidence varies substantially across applications. Computed tomography-derived and angiography-derived physiological assessment, selected automated image-analysis tools, and operator-controlled robotic assistance have demonstrated prospective or real-world evidence in specific settings, while digital twins, hybrid intravascular ultrasound-optical coherence tomography interpretation, generative simulations, autonomous robotic control, and large language model-based reasoning remain at earlier validation stages. This review examines AI applications throughout the interventional cardiovascular care pathway and critically evaluates supporting evidence, while addressing translational barriers including dataset heterogeneity, workflow integration, cost-effectiveness, cybersecurity, regulatory oversight, and clinician trust. Rather than viewing AI monolithically, the authors propose an evidence-calibrated framework distinguishing clinically implemented tools from investigational platforms. Future adoption should prioritize prospective validation, transparent evidence grading, reproducibility, regulatory clarity, and demonstrable patient-centered outcomes over algorithmic novelty alone. As interventional cardiology advances toward increasingly complex catheter-based therapies, AI's clinical value depends on specific task validation and outcome improvement rather than computational performance.