Artificial Intelligence in Cardiovascular Imaging: Integration with Clinical and Nonimaging Data

Artificial intelligence (AI) is increasingly transforming cardiovascular imaging, extending beyond automated image analysis to the integration of imaging with diverse clinical and nonimaging data. This multimodal approach has the potential to enhance diagnostic accuracy, improve risk stratification, and support personalized management across a wide range of cardiovascular diseases. In this review, we discuss the role of AI in integrating cardiovascular imaging data with complementary information sources, including clinical and demographic variables, laboratory biomarkers, electrocardiographic data, genetic and omics profiles, electronic health records, and data derived from wearable devices. Machine learning and deep learning techniques enable the analysis of these heterogeneous datasets, facilitating the identification of complex patterns that are not readily captured by traditional analytical methods. We highlight the potential applications of multimodal AI models in major cardiovascular conditions, including coronary artery disease, heart failure, valvular heart disease, and cardiomyopathies. By combining information from imaging modalities such as echocardiography, computed tomography, and cardiac magnetic resonance, with nonimaging data, AI may improve disease detection, prognostic assessment, and therapeutic decision-making. Finally, we outline the key challenges related to data integration, model interpretability, generalizability, and ethical considerations. The continued development of AI-driven multimodal frameworks is expected to play an important role in advancing precision cardiovascular medicine and maximizing the clinical utility of cardiovascular imaging.