Revolutionizing AI-driven Hypertension Care: A Review of Current Trends and Future Directions

Abstract

Almost all countries have patients with hypertension as a standard but far-reaching medical concern, and this brings notable financial consequences. The combination of Artificial Intelligence and Machine Learning in controlling hypertension holds the potential for timely recognition, individualized management approaches, and adherence to medication monitoring. Nevertheless, healthcare faces hurdles in adopting such technologies due to data quality, system integration, ethical considerations, and regulatory barriers. This literature review mainly deals with the current state of AI and ML use in the management of hypertension. Particular attention is paid to their prediction, monitoring, and individualization of the therapeutic approaches. Key areas of interest include early detection, risk prediction, and developing individualized care plans. To promote the responsible and ethical use of AI in healthcare, future research in this field might include but not be limited to continuous monitoring, chronic disease management, and the integration of multi-modal data. Patient privacy, data security, algorithmic bias, and informed consent are the ethical issues to consider. Furthermore, the review discusses the ethical dilemmas surrounding patient privacy, data security, and programming biases in AI-driven healthcare solutions. To ensure that these technologies are effectively implemented in clinical practice, we need to address issues relating to data quality, system integration, ethics, and regulation. This may have potential results such as transforming hypertension management through sustained innovation efforts, thus improving quality care among hypertensive patients. Finally, the review highlights the future potential of AI to transform clinical practice, individualize treatment approaches, and mitigate the global impact of hypertension on public health.

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