The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

人工智能 (AI) 的兴起彻底改变了各个科学领域，特别是在医学领域，它使得能够根据海量数据集对复杂关系进行建模。最初，人工智能算法除了预测患者结果和未来疾病发作外，还专注于改进对胸部 X 光和心电图等诊断研究的解释。然而，人工智能随着变压器模型的引入而不断发展，允许分析当今医学中存在的多样化、多模式数据源。多模态人工智能在更准确的疾病风险评估和分层以及优化心脏代谢疾病的关键驱动因素（血压、睡眠、压力、血糖控制、体重、营养和体力活动）方面前景广阔。在本文中，我们概述了医疗人工智能在心脏代谢疾病中的现状，强调了多模式人工智能在增强心脏代谢疾病个性化预防和治疗策略方面的潜力。