Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension

Area of Science:

• Artificial Intelligence in Healthcare
• Machine Learning Applications
• Digital Therapeutics

Background:

• Artificial intelligence (AI) advancements are transforming various fields, notably healthcare.
• Key AI technologies include supervised and unsupervised machine learning, utilizing neural networks and deep learning.
• Natural Language Processing (NLP) enables AI to understand and generate human language for applications like chatbots.

Purpose of the Study:

• To explore the impact of AI breakthroughs on healthcare.
• To highlight the role of machine learning and NLP in AI development.
• To examine AI's potential in hypertension management.

Main Methods:

• Leveraging supervised and unsupervised machine learning with neural networks and deep learning.
• Utilizing Natural Language Processing for human-like interaction and information generation.
• Applying computer vision for new imaging models.

Main Results:

• AI models can process information and perform tasks comparable to or exceeding human capabilities.
• Increased computing power and large datasets have enabled advanced AI models like large language models (e.g., ChatGPT).
• AI facilitates hypertension management through analysis of biometric data and patient communication.

Conclusions:

• AI, particularly machine learning and NLP, holds significant potential to enhance hypertension diagnosis and treatment.
• Remote patient monitoring and digital therapeutics powered by AI can improve patient outcomes.
• AI integration promises more efficient and effective healthcare delivery.