{"title":"Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management.","authors":"Evan D Muse, Eric J Topol","doi":"10.1016/j.cmet.2024.02.002","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":93927,"journal":{"name":"Cell metabolism","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10990799/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell metabolism","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cmet.2024.02.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/29 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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)的兴起给各个科学领域带来了革命性的变化,尤其是在医学领域,它能够从海量数据集中建立复杂关系的模型。最初,人工智能算法除了预测病人的预后和未来疾病的发病情况外,还侧重于改进对胸部 X 光片和心电图等诊断研究的解释。然而,随着变压器模型的引入,人工智能也在不断发展,从而可以对当今医学中存在的各种多模态数据源进行分析。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、营养和体育锻炼)方面大有可为。在这篇文章中,我们概述了医疗人工智能在心血管代谢疾病中的应用现状,强调了多模态人工智能在增强心血管代谢疾病个性化预防和治疗策略方面的潜力。