AI, Machine Learning, and ChatGPT in Hypertension.

Area of Science:

• Cardiovascular Medicine
• Artificial Intelligence
• Biomedical Data Science

Background:

• Hypertension is a primary driver of cardiovascular disease and mortality, yet effective blood pressure control remains challenging.
• Despite available pharmacological treatments, a significant need exists for improved hypertension management strategies.
• Artificial intelligence (AI) is emerging as a promising tool to address these challenges in cardiovascular medicine.

Purpose of the Study:

• To introduce machine learning (ML) concepts relevant to cardiovascular medicine.
• To review current and potential applications of ML in hypertension research and clinical practice.
• To encourage the integration of ML into hypertension research for developing advanced diagnostic and therapeutic tools.

Main Methods:

• Review of existing literature on machine learning applications in hypertension.
• Discussion of ML's role in disease diagnosis, prognosis, and treatment decision-making.
• Analysis of ML techniques for omics data integration and interpretation in hypertension.

Main Results:

• Machine learning demonstrates potential in analyzing complex datasets for hypertension.
• Applications include enhanced disease diagnosis, prognosis prediction, and personalized treatment recommendations.
• ML can integrate diverse data types, including genomics, socioeconomic, behavioral, and environmental factors, for precise risk prediction.

Conclusions:

• Machine learning holds significant promise for advancing hypertension management and research.
• Integrating ML with traditional risk factors and novel data sources can lead to personalized medicine approaches.
• Further exploration and adoption of ML are crucial for developing innovative diagnostic and therapeutic tools for hypertension.