Large language models in interventional cardiology: current evidence and future directions

Large language models (LLMs) have rapidly emerged as a transformative class of artificial intelligence systems capable of understanding and generating human-like text from vast corpora of clinical and scientific literature. While their use in general cardiology has been extensively reviewed, their specific role in interventional cardiology and within the catheterization laboratory (cath lab) environment remains less well characterized. This narrative review synthesizes the current evidence on LLM applications across the interventional cardiology workflow, including coronary revascularization decision-making, multidisciplinary Heart Team support, structural heart intervention planning, periprocedural communication, and acute cath lab decision support. Recent studies suggest that contemporary LLMs such as ChatGPT-4 (OpenAI), Claude (Anthropic), and Gemini (Alphabet, Inc.) can achieve clinically meaningful concordance with expert Heart Team recommendations for percutaneous coronary intervention vs coronary artery bypass grafting, and that their outputs may approach or even match those of early-career interventional cardiologists in simulated emergency scenarios. However, important limitations persist, including variable accuracy across prompt formats, susceptibility to hallucinations, lack of multimodal integration with angiographic and intravascular imaging, opacity of training sources, and unresolved medicolegal issues. The authors discuss the current evidence base, highlight promising avenues such as multimodal LLMs and retrieval-augmented generation tools that leverage current guidelines, and outline regulatory and ethical considerations that must accompany clinical adoption. LLMs are unlikely to replace interventional cardiologists in the foreseeable future, but they may meaningfully support decision-making, education, and patient communication in a domain where speed, complexity, and risk converge.