Dziopa Katarzyna, Lekadir Karim, van der Harst Pim, Asselbergs Folkert W
Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands; Institute of Health Informatics, University College London, London, United Kingdom; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom.
Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Department of Mathematics and Computer Science, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain.
Hellenic J Cardiol. 2025 Jan-Feb;81:4-8. doi: 10.1016/j.hjc.2024.06.001. Epub 2024 Jun 7.
The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the potential to redefine cardiovascular risk prevention. Digital twinning combines vast amounts of data from diverse sources to construct virtual models of an individual. Emerging artificial intelligence models, called generalist AI, enable the processing of different types of data, including data from electronic health records, laboratory results, medical texts, imaging, genomics, or graphs. Among their unprecedented capabilities are an easy adaptation of a model to previously unseen medical tasks and the ability to reason and explain output using precise medical language derived from scientific literature, medical guidelines, or knowledge graphs. The proposed combination of a digital twinning approach with generalist AI is a path to accelerate the implementation of precision medicine and enhance early recognition and prevention of cardiovascular disease. This proposed strategy may extend to other domains to advance predictive, preventive, and precision medicine and also boost health research discoveries.
高度适应性和可重复使用的人工智能模型的快速发展促进了数字孪生的实施，并有可能重新定义心血管疾病风险预防。数字孪生结合了来自不同来源的大量数据，以构建个体的虚拟模型。新兴的人工智能模型，即通用人工智能，能够处理不同类型的数据，包括来自电子健康记录、实验室结果、医学文本、成像、基因组学或图表的数据。其前所未有的能力包括轻松将模型应用于以前未见过的医疗任务，以及使用源自科学文献、医学指南或知识图谱的精确医学语言对输出进行推理和解释的能力。将数字孪生方法与通用人工智能相结合的提议是加速精准医学实施、加强心血管疾病早期识别和预防的一条途径。这一提议的策略可能会扩展到其他领域，以推进预测性、预防性和精准医学，并促进健康研究发现。
