Shimbo Daichi, Shah Rashmee U, Abdalla Marwah, Agarwal Ritu, Ahmad Faraz S, Anaya Gabriel, Attia Zachi I, Bull Sheana, Chang Alexander R, Commodore-Mensah Yvonne, Ferdinand Keith, Kawamoto Kensaku, Khera Rohan, Leopold Jane, Luo James, Makhni Sonya, Mortazavi Bobak J, Oh Young S, Savage Lucia C, Spatz Erica S, Stergiou George, Turakhia Mintu P, Whelton Paul K, Yancy Clyde W, Iturriaga Erin
Department of Medicine, Columbia University Irving Medical Center, New York, NY (D.S., M.A.).
Division of Cardiovascular Medicine (R.U.S.), University of Utah School of Medicine, Salt Lake City.
Hypertension. 2025 Jan;82(1):36-45. doi: 10.1161/HYPERTENSIONAHA.124.22095. Epub 2024 Jul 16.
Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.
高血压是心血管疾病、慢性肾脏病和痴呆症最重要的风险因素之一。人工智能(AI)领域发展迅速,但关于如何利用AI改善高血压的诊断和管理的讨论却很少。包括机器学习工具在内的AI技术可能会改变我们诊断和管理高血压的方式,对改善个人和人群健康具有潜在影响。在公共卫生和医疗保健系统中开发成功的AI工具需要临床医生、工程师和数据科学家之间建立合作关系,并具备多种专业知识。无偏差的数据源、管理和分析仍然是一个基础性挑战。从诊断角度来看,机器学习工具可能会改善血压测量,并有助于预测高血压的发生。为了推进高血压的管理,机器学习工具可能有助于通过分析预测患者对降压药物的反应以及高血压相关并发症的风险,从而为患者找到个性化治疗方案。然而,在高血压领域使用AI工具存在实际实施方面的挑战。在此,我们总结了2023年3月参加美国国立心肺血液研究所举办的研讨会的不同利益相关者的主要发现。研讨会参与者介绍了临床医学、数据科学和医疗保健工程之间的沟通差距信息;估计血压、高血压风险和血压控制的新方法;以及实际实施方面的挑战和问题。