Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
Imperial College London School of Medicine, London, SW7 2AZ, UK.
Vasc Health Risk Manag. 2022 Jul 12;18:517-528. doi: 10.2147/VHRM.S279337. eCollection 2022.
Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.
心血管疾病（CVD）对医疗系统构成了重大且日益增加的负担。人工智能（AI）是一个快速发展的跨学科领域，采用机器学习（ML）技术，旨在模拟人类直觉，提供具有成本效益和可扩展性的解决方案，以更好地管理 CVD。ML 算法越来越多地被开发并应用于心血管医学的各个方面，包括但不限于心力衰竭、电生理学、瓣膜性心脏病和冠状动脉疾病。在心力衰竭中，AI 算法可以通过自动心脏测量来增强诊断能力和临床决策。使用 ML 从诊断数据中可以越来越多地识别隐匿性心脏病。使用 ML 算法提高诊断和预后能力，可增强瓣膜性心脏病和冠状动脉疾病患者的临床护理。AI 技术的发展并非没有内在的挑战，其中最重要的是需要通过多中心、前瞻性临床试验进行更多的外部验证。
