Artificial intelligence (AI) and in silico models, in conjunction with the rapid adoption of mobile health (mHealth) technologies such as smart wearables, have the potential to revolutionize the monitoring, screening, and treatment of cardiovascular disease patients. Broadly speaking, AI and machine learning (ML) are predominantly statistical methods—learning from patient data to predict outcomes, with often little to no mechanistic understanding of the underlying processes. On the other hand, the nascent but rapidly developing field of in silico models are mechanistic models—they use the underlying physics/chemistry to model the phenomenon of interest be that stroke and its treatment,¹ heart failure,² or cardiotoxicity.³ A typical example of an in silico model we use daily is the weather forecast—where the equations of weather formation are used in conjunction with previously collected data to make predictions on how the weather will develop. A synergistic use of both these statistical and mechanistic models will have the greatest value in aiding patient evaluation and treatment.

中文翻译：

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“机器的崛起”：个体化医疗的下一个前沿

人工智能 (AI) 和计算机模型，连同智能可穿戴设备等移动健康 (mHealth) 技术的快速采用，有可能彻底改变心血管疾病患者的监测、筛查和治疗。从广义上讲，人工智能和机器学习 (ML) 主要是统计方法——从患者数据中学习以预测结果，通常对基本过程几乎没有机械理解。另一方面，计算机模型的新生但快速发展的领域是机械模型——它们使用基础物理/化学来模拟感兴趣的现象，即中风及其治疗、¹心力衰竭、²或心脏毒性。^{3我们每天使用}的计算机模型的一个典型例子是天气预报——天气形成方程与先前收集的数据结合使用，以预测天气将如何发展。这些统计和机械模型的协同使用将在帮助患者评估和治疗方面具有最大价值。