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Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

  • Prevention of Hypertension: Public Health Challenges (Y Yano, Section Editor)
  • Published:
Current Hypertension Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Evidence that artificial intelligence (AI) is useful for predicting risk factors for hypertension and its management is emerging. However, we are far from harnessing the innovative AI tools to predict these risk factors for hypertension and applying them to personalized management. This review summarizes recent advances in the computer science and medical field, illustrating the innovative AI approach for potential prediction of early stages of hypertension. Additionally, we review ongoing research and future implications of AI in hypertension management and clinical trials, with an eye towards personalized medicine.

Recent Findings

Although recent studies demonstrate that AI in hypertension research is feasible and possibly useful, AI-informed care has yet to transform blood pressure (BP) control. This is due, in part, to lack of data on AI’s consistency, accuracy, and reliability in the BP sphere. However, many factors contribute to poorly controlled BP, including biological, environmental, and lifestyle issues. AI allows insight into extrapolating data analytics to inform prescribers and patients about specific factors that may impact their BP control.

Summary

To date, AI has been mainly used to investigate risk factors for hypertension, but has not yet been utilized for hypertension management due to the limitations of study design and of physician’s engagement in computer science literature. The future of AI with more robust architecture using multi-omics approaches and wearable technology will likely be an important tool allowing to incorporate biological, lifestyle, and environmental factors into decision-making of appropriate drug use for BP control.

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Authors and Affiliations

  1. Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, Tenth Avenue, Suite 3A-09, New York, NY, 10019, USA

    Chayakrit Krittanawong

  2. Division of Nephrology, College of Physicians and Surgeons, Columbia University, New York, NY, USA

    Andrew S. Bomback

  3. Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Usman Baber

  4. Department of Cardiovascular Medicine, NYU Langone Health, School of Medicine, New York University, New York, NY, USA

    Sripal Bangalore

  5. Mount Sinai Icahn School of Medicine, New York, NY, USA

    Franz H. Messerli

  6. University of Bern, Bern, Switzerland

    Franz H. Messerli

  7. Jagiellonian University Krakow, Krakow, Poland

    Franz H. Messerli

  8. Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland, OH, USA

    W. H. Wilson Tang

  9. Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland, OH, USA

    W. H. Wilson Tang

  10. Center for Clinical Genomics, Cleveland Clinic, Cleveland, OH, USA

    W. H. Wilson Tang

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  1. Chayakrit Krittanawong
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  4. Sripal Bangalore
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Correspondence to Chayakrit Krittanawong.

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This article is part of the Topical Collection on Prevention of Hypertension: Public Health Challenges

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Krittanawong, C., Bomback, A.S., Baber, U. et al. Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension. Curr Hypertens Rep 20, 75 (2018). https://doi.org/10.1007/s11906-018-0875-x

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