Large language models in cardiovascular imaging: Current applications and future prospects

The integration of LLMs across cardiovascular imaging modalities promises transformative advancements, contingent upon overcoming significant technical and ethical hurdles. Key developments include the evolution of LLMs in nuclear medicine to provide comprehensive interpretation of quantitative perfusion data by synergizing with deep learning systems, and their application in cardiac magnetic resonance for automated tissue characterization that correlates fibrosis patterns with functional parameters. In coronary computed tomography angiography, the fusion of anatomical plaque assessment with clinical risk prediction will enable personalized protocol optimization, whereas echocardiography will leverage LLMs for real-time procedural guidance through dynamic interpretation of Doppler flow patterns, utilizing federated learning to ensure data privacy. Realizing this potential mandates the creation of harmonized multimodal datasets and robust validation pathways incorporating clinician-in-the-loop benchmarking. Crucially, a rigorous ethical framework is imperative, requiring advanced de-identification techniques with data provenance tracking, real-time uncertainty quantification to manage diagnostic errors, and algorithmic transparency standards. The pace of adoption ultimately depends on resolving technical barriers in multimodal data fusion and interoperability, while simultaneously addressing these ethical considerations through collaborative efforts to establish standardized clinically-grounded validation protocols.