The field of cardiology is witnessing a significant shift towards leveraging natural language processing (NLP) techniques to analyze and extract insights from vast amounts of unstructured data. This includes patient narratives, medical records, and scientific literature, which can provide valuable information on the complex interrelationships between genetic predispositions, lifestyle choices, and socioeconomic and clinical factors that influence heart-related conditions. Recent research has focused on developing and applying NLP methods to improve diagnosis, treatment, and prevention of cardiac problems. Notably, the use of large language models and deep learning-based approaches has shown promising results in extracting relevant information from clinical texts, including disease and medication mentions, treatment regimens, and toxicity documentation. The application of these techniques has the potential to revolutionize current approaches to cardiology and support advancements in data-driven clinical systems. Noteworthy papers include: The paper on Multilingual Clinical NER for Diseases and Medications Recognition in Cardiology Texts using BERT Embeddings, which achieved high F1-scores for disease and medication recognition in multiple languages. The paper on Automated Extraction of Fluoropyrimidine Treatment and Treatment-Related Toxicities from Clinical Notes Using Natural Language Processing, which demonstrated the effectiveness of large language models in extracting treatment and toxicity information from clinical notes.