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Cardiovascular care with digital twin technology in the era of generative artificial intelligence

Thangaraj, Phyllis M; Benson, Sean H; Oikonomou, Evangelos K; Asselbergs, Folkert W; Khera, Rohan; (2024) Cardiovascular care with digital twin technology in the era of generative artificial intelligence. European Heart Journal 10.1093/eurheartj/ehae619. (In press).

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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.

Type: Article
Title: Cardiovascular care with digital twin technology in the era of generative artificial intelligence
Location: England
DOI: 10.1093/eurheartj/ehae619
Publisher version: http://dx.doi.org/10.1093/eurheartj/ehae619
Language: English
Keywords: Digital twins, Generative artificial intelligence, Multi-modal models, Precision medicine
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10199941
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