TY - JOUR
T1 - Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence
T2 - A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report
AU - Shimbo, Daichi
AU - Shah, Rashmee U.
AU - Abdalla, Marwah
AU - Agarwal, Ritu
AU - Ahmad, Faraz S.
AU - Anaya, Gabriel
AU - Attia, Zachi I.
AU - Bull, Sheana
AU - Chang, Alexander R.
AU - Commodore-Mensah, Yvonne
AU - Ferdinand, Keith
AU - Kawamoto, Kensaku
AU - Khera, Rohan
AU - Leopold, Jane
AU - Luo, James
AU - Makhni, Sonya
AU - Mortazavi, Bobak J.
AU - Oh, Young S.
AU - Savage, Lucia C.
AU - Spatz, Erica S.
AU - Stergiou, George
AU - Turakhia, Mintu P.
AU - Whelton, Paul K.
AU - Yancy, Clyde W.
AU - Iturriaga, Erin
N1 - Publisher Copyright:
© 2024 Lippincott Williams and Wilkins. All rights reserved.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - blood pressure
KW - delivery of health care
KW - hypertension
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85199045104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85199045104&partnerID=8YFLogxK
U2 - 10.1161/HYPERTENSIONAHA.124.22095
DO - 10.1161/HYPERTENSIONAHA.124.22095
M3 - Review article
C2 - 39011653
AN - SCOPUS:85199045104
SN - 0194-911X
JO - Hypertension
JF - Hypertension
ER -