Artificial intelligence in the cath lab: Bridging predictive models and real-time procedural decision support

The integration of Artificial Intelligence (AI) in medicine has been revolutionary, particularly in cardiology, where AI offers transformative tools for data integration, image analysis, and predictive modeling. In procedural settings such as percutaneous coronary intervention (PCI) planning, where timely decision-making is crucial, AI represents a promising avenue for real-time risk prediction. However, current clinical scores and risk models often fall short in dynamic environments like the catheterization (cath) lab due to their static nature and limited adaptability to intra-procedural complexities. Emerging AI models aim to leverage high-frequency physiological data, procedural metadata, and multimodal imaging to recognize evolving patterns and anticipate complications. Nevertheless, most existing applications remain retrospective, lack real-time integration, and are constrained by limited external validation. Looking ahead, the implementation of real-time AI systems in the cath lab holds significant potential to enhance procedural safety and outcomes by delivering anticipatory alerts and actionable insights that support clinical decision-making during PCI. However, it is important to note that most currently available AI models remain retrospective or observational in nature, and prospective evidence demonstrating improved clinical outcomes through real-time AI-guided interventions remains limited. See also the graphical abstract(Fig. 1).