Phyllis M Thangaraj, Sean H Benson, Evangelos K Oikonomou, Folkert W Asselbergs, Rohan Khera
{"title":"生成式人工智能时代的数字孪生技术与心血管护理","authors":"Phyllis M Thangaraj, Sean H Benson, Evangelos K Oikonomou, Folkert W Asselbergs, Rohan Khera","doi":"10.1093/eurheartj/ehae619","DOIUrl":null,"url":null,"abstract":"Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.","PeriodicalId":11976,"journal":{"name":"European Heart Journal","volume":null,"pages":null},"PeriodicalIF":37.6000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cardiovascular care with digital twin technology in the era of generative artificial intelligence\",\"authors\":\"Phyllis M Thangaraj, Sean H Benson, Evangelos K Oikonomou, Folkert W Asselbergs, Rohan Khera\",\"doi\":\"10.1093/eurheartj/ehae619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. 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Cardiovascular care with digital twin technology in the era of generative artificial intelligence
Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.
期刊介绍:
The European Heart Journal is a renowned international journal that focuses on cardiovascular medicine. It is published weekly and is the official journal of the European Society of Cardiology. This peer-reviewed journal is committed to publishing high-quality clinical and scientific material pertaining to all aspects of cardiovascular medicine. It covers a diverse range of topics including research findings, technical evaluations, and reviews. Moreover, the journal serves as a platform for the exchange of information and discussions on various aspects of cardiovascular medicine, including educational matters.
In addition to original papers on cardiovascular medicine and surgery, the European Heart Journal also presents reviews, clinical perspectives, ESC Guidelines, and editorial articles that highlight recent advancements in cardiology. Additionally, the journal actively encourages readers to share their thoughts and opinions through correspondence.