Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Arterial hypertension (AH) is a growing global health concern, particularly with an aging population.
• The increasing prevalence of AH necessitates innovative approaches for effective management and treatment.
• Artificial intelligence (AI) is recognized for its potential to revolutionize personalized medicine in AH.

Purpose of the Study:

• To review the benefits and limitations of AI applications in the prevention and treatment of arterial hypertension.
• To explore how AI can enhance diagnostic accuracy and therapeutic strategies for AH patients.
• To identify AI-driven advancements in personalized medicine for AH management.

Main Methods:

• A comprehensive literature search was conducted using databases like PubMed and Google Scholar.
• Search terms included "artificial intelligence," "arterial hypertension," "deep learning," "machine learning," and "personalized medicine."
• Reviewed articles focused on quantitative and qualitative AI applications in AH management.

Main Results:

• AI enables continuous blood pressure (BP) monitoring via wearable devices, estimating BP from photoplethysmograph (PPG) signals using deep learning (DL).
• Machine learning (ML) algorithms can identify novel hypertension genes for early diagnosis and complication prevention.
• Integrating AI with omics technologies aids in defining patient trajectories and optimizing drug selection.

Conclusions:

• AI-based systems are poised to transform clinical practice for AH by enabling personalized care plans and predicting patient risks.
• AI facilitates risk stratification and timely therapy adjustments based on disease progression and treatment response.
• Addressing technical challenges like algorithm bias and data privacy is crucial for widespread AI adoption in AH.