What is the clinical evidence from this study?

Study design: Review. Population: Hypertension. Intervention: Artificial intelligence and digital technologies.

What question did this study set out to answer?

To evaluate recent advancements, clinical applications, and implementation challenges of artificial intelligence across the continuum of hypertension care.

March 28, 2026Clinical HypertensionOpen Access

Current perspectives and challenges of digital hypertension: artificial intelligence in the management of hypertension

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Key result

AI and digital technologies reshape hypertension management via precision phenotyping and personalized remote interventions.

Why the study?

Conventional strategies have failed to achieve adequate blood pressure control rates due to inadequate cardiovascular risk recognition and suboptimal early-stage diagnosis and phenotyping, underscoring an urgent need for transformative innovations in hypertension management.

Key Points

To evaluate recent advancements, clinical applications, and implementation challenges of artificial intelligence across the continuum of hypertension care.
Structured analysis of artificial intelligence workflows spanning risk prediction, phenotyping, continuous blood pressure monitoring, and personalized clinical decision support.
Synthesis of machine learning applications integrating multimodal data inputs, including electronic health records, wearable sensor metrics, and patient-reported outcomes.
Conventional approaches achieve blood pressure control rates of only 59% in Korea, 43.5% in the United States, and 12.9% in China among affected populations.
Artificial intelligence models facilitate continuous cuffless blood pressure waveform reconstruction, detection of masked and white-coat hypertension, and drug response forecasting.
Implementation remains constrained by challenges in clinical validation, algorithmic equity, and a need for large-scale clinical trials to establish real-world utility.

PICO

Population

Hypertension

Intervention / Comparator

Artificial intelligence and digital technologies

AI and digital health technologies are emerging as powerful tools for personalized hypertension management, but require rigorous prospective validation and equitable data representation before widespread clinical adoption.

Limitations

Data quality, complexity, and representativeness issues
Most AI models are trained on datasets from high-income countries, risking algorithmic bias
Lack of prospective clinical validation and universal validation standards for cuffless devices
Practical barriers including clinician skepticism, EHR interoperability, and ambiguous reimbursement policies
Lack of prospective validation for clinical utility
Models predominantly trained on datasets from high-income countries, risking algorithmic bias
Lack of universal validation standards for cuffless blood pressure monitors
Practical barriers to adoption including clinician skepticism, EHR interoperability, and ambiguous reimbursement policies

Implication

Should not yet change routine hypertension practice; leaves open needs for prospective validation and equitable data representation.

Abstract

Hypertension remains a leading global health challenge.However, decades of conventional strategies have failed to achieve adequate blood pressure (BP) control rates.Given the complex pathophysiology and diverse risk factors of hypertension, the primary barriers to its effective management are an inadequate recognition of cardiovascular risk, as well as suboptimal methods for accurate, early-stage diagnosis and phenotyping.This persistent public health problem underscores an urgent need for transformative innovations of hypertension management.Digital hypertension is an emerging field that integrates advanced technologies into the management of hypertension, especially artificial intelligence (AI).Emerging technologies are providing new strategies for hypertension management, including accurate prediction and precision subtyping from large datasets, remote telemedicine support, intelligent BP monitoring, and personalized interventions.This review summarizes recent advancements and challenges in these technologies, particularly AI, offering new insights for realizing precision medicine in the era of digital hypertension.

Discussion

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Published by Clinical Hypertension · CC BY-NC

Cite This Study

Yin et al. (2026) conducted a review in Hypertension. Artificial intelligence and digital technologies was evaluated. Artificial intelligence and digital technologies are reshaping hypertension management by enabling accurate risk prediction, precision phenotyping, and personalized remote interventions.

synapsesocial.com/papers/69c771c58bbfbc51511e1c7a https://doi.org/10.5646/ch.2026.32.e17

Topics

Hypertension management

Artificial intelligence in cardiology

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Implication

Should not yet change routine hypertension practice; leaves open needs for prospective validation and equitable data representation.

Current perspectives and challenges of digital hypertension: artificial intelligence in the management of hypertension

Key Points

PICO

Limitations

Implication

Abstract

Discussion

Figures from the paper

Cite This Study

Topics

Current perspectives and challenges of digital hypertension: artificial intelligence in the management of hypertension

Key Points

PICO

Limitations

Implication

Abstract

Discussion

Figures from the paper

Cite This Study

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