Transformer models in cardiovascular medicine: current applications, translational challenges, and future directions

Abstract

Cardiovascular diseases remain a leading cause of mortality and morbidity worldwide, highlighting the need for improved early detection, accurate diagnosis, and personalized therapeutic strategies. With advances in artificial intelligence (AI), Transformer-based models have attracted increasing interest because they can model long-range dependencies, support large-scale pre-training, and integrate heterogeneous data. Originally developed for natural language processing, Transformers have been extended to medical image analysis, physiological signal interpretation, and clinical text mining. In cardiovascular medicine, these models have been applied to electrocardiogram interpretation, cardiac imaging, electronic health record analysis, multimodal data integration, digital-twin development, and large language model-enabled workflows. Although many studies report promising performance, clinical value remains contingent on data quality, external validation, calibration, workflow integration, and trustworthy governance. This narrative review provides a focused overview of Transformer-based models in cardiovascular medicine, critically evaluates their translational limitations, and outlines priorities for clinically useful and responsible cardiovascular AI.