Kasartzian Dimitrios-Ioannis, Tsiampalis Thomas
Department of Nutrition and Dietetics, School of Physical Education, Sports and Dietetics, University of Thessaly, 42132 Trikala, Greece.
Life (Basel). 2025 Jan 14;15(1):94. doi: 10.3390/life15010094.
Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intelligence (AI) approaches represent a paradigm shift in risk prediction, offering dynamic, scalable solutions that integrate diverse data types. This review examines advancements in AI/ML for CVD risk prediction, analyzing their strengths, limitations, and the challenges associated with their clinical integration. Recommendations for standardization, validation, and future research directions are provided to unlock the potential of these technologies in transforming precision cardiovascular medicine.
心血管疾病（CVDs）仍然是全球死亡率和发病率的主要原因。传统的风险预测模型虽然是基础，但往往无法捕捉风险因素的多方面性质，也无法利用不断扩大的医疗保健数据池。机器学习（ML）和人工智能（AI）方法代表了风险预测的范式转变，提供了整合多种数据类型的动态、可扩展解决方案。本综述探讨了用于CVD风险预测的AI/ML进展，分析了它们的优势、局限性以及与临床整合相关的挑战。提供了标准化、验证和未来研究方向的建议，以释放这些技术在改变精准心血管医学方面的潜力。
