Cardiovascular diseases are becoming increasingly prevalent in modern society, with a profound impact on global health and well-being. These Cardiovascular disorders are complex and multifactorial, influenced by genetic predispositions, lifestyle choices, and diverse socioeconomic and clinical factors. Information about these interrelated factors is dispersed across multiple types of textual data, including patient narratives, medical records, and scientific literature. Natural language processing (NLP) has emerged as a powerful approach for analysing such unstructured data, enabling healthcare professionals and researchers to gain deeper insights that may transform the diagnosis, treatment, and prevention of cardiac disorders. This review provides a comprehensive overview of NLP research in cardiology from 2014 to 2025. We systematically searched six literature databases for studies describing NLP applications across a range of cardiovascular diseases. After a rigorous screening process, we identified 265 relevant articles. Each study was analysed across multiple dimensions, including NLP paradigms, cardiology-related tasks, disease types, and data sources. Our findings reveal substantial diversity within these dimensions, reflecting the breadth and evolution of NLP research in cardiology. A temporal analysis further highlights methodological trends, showing a progression from rule-based systems to large language models. Finally, we discuss key challenges and future directions, such as developing interpretable LLMs and integrating multimodal data. To the best of our knowledge, this review represents the most comprehensive synthesis of NLP research in cardiology to date.

翻译：心血管疾病在现代社会日益普遍，对全球健康和福祉产生深远影响。这些心血管疾病具有复杂性和多因素性，受遗传易感性、生活方式选择以及多种社会经济和临床因素的综合影响。关于这些相互关联因素的信息分散于多种类型的文本数据中，包括患者叙述、医疗记录和科学文献。自然语言处理（NLP）已成为分析此类非结构化数据的有力方法，使医疗专业人员和研究人员能够获得更深入的见解，从而可能改变心脏疾病的诊断、治疗和预防方式。本综述全面概述了2014年至2025年心脏病学领域的NLP研究。我们系统检索了六个文献数据库，以收集描述NLP在各种心血管疾病中应用的研究。经过严格筛选，我们确定了265篇相关文章。每项研究均从多个维度进行分析，包括NLP范式、心脏病学相关任务、疾病类型和数据来源。我们的研究结果揭示了这些维度内的显著多样性，反映了心脏病学NLP研究的广度和演变历程。时间分析进一步突显了方法学趋势，显示出从基于规则的系统到大型语言模型的演进。最后，我们讨论了关键挑战和未来方向，例如开发可解释的大型语言模型以及整合多模态数据。据我们所知，本综述代表了迄今为止对心脏病学NLP研究最全面的综合。