Why the study?
Blood pressure control remains suboptimal due to episodic measurements and population-based algorithms, which artificial intelligence could address by integrating longitudinal, multimodal data.
Comparison
Application of AI across the hypertension care continuum vs conventional approaches
Design
Systematic review
Key result
Artificial intelligence has the potential to shift hypertension management from a reactive, threshold-based paradigm toward a more predictive, personalized, and patient-centered model.
Authors
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Supports predictive AI use in hypertension; extends evidence but leaves open outcome validation before routine adoption.
Artificial intelligence offers a transformative framework to address limitations in hypertension management by enabling the integration of longitudinal, multimodal data for predictive and personalized care.
Varzideh et al. (2026) studied this question. Artificial intelligence has the potential to shift hypertension management from a reactive, threshold-based paradigm toward a more predictive, personalized, and patient-centered model.
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