Key result
AI and ML tools may improve BP measurement, predict incident hypertension, and personalize treatments.
Why the study?
Despite rapid advances in artificial intelligence, there has been little discussion on how AI could be leveraged to improve the diagnosis and management of hypertension.
This NHLBI workshop report outlines the potential of AI to improve hypertension diagnosis and management while highlighting the need for unbiased data and multidisciplinary collaboration.
AI tools remain investigational for hypertension; leaves open need for validation trials and unbiased data before practice adoption.
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.
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Shimbo et al. (2024) conducted a review in Hypertension. Artificial intelligence (AI) technologies was evaluated. Artificial intelligence and machine learning tools show potential to improve blood pressure measurement, predict incident hypertension, and personalize treatments, despite implementation challenges.
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