Patrick Dunn, Asif Ali, Akash P Patel, Srikanta Banerjee
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Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.
Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.
期刊介绍:
Hypertension presents top-tier articles on high blood pressure in each monthly release. These articles delve into basic science, clinical treatment, and prevention of hypertension and associated cardiovascular, metabolic, and renal conditions. Renowned for their lasting significance, these papers contribute to advancing our understanding and management of hypertension-related issues.