Medical & Clinical Research

Author(s): Lainjo B

Hypertension affects over 1 billion people, highlighting its significance as a major public health concern. AI holds considerable promise in healthcare by enabling data analysis, risk assessment, and the customization of individual treatment plans. This research investigates the patterns, outcomes, and collaborations in AI studies related to hypertension from 2013 to 2023. Structured questions were used with the Web of Science, Scopus, and PubMed databases. Frequency analysis was carried out to describe publication trends, while collaboration networks were studied using VOSviewer and Gephi. Thematic analysis for research focus areas was executed through clustering based on the identified keywords. Quantitative analysis of the academic literature confirmed that the AI and hypertension literature has rapidly expanded with a compound annual growth rate (CAGR) of 12.5%. Specific institutions contributing to this research include institutions from the US, China and the UK because the establishment formed most of the co-authorship network. The prominent areas pointed to the new trends in machine learning for risk assessment, wearable technologies, and AI for equality. However, limited studies have involved low- and middleincome countries (LMICs). The use of AI in hypertension studies is expanding, and this development has provided essential findings for risk assessment and individual patient management. However, research contributions are distributed unequally, and there are very few real-life applications for practice. Subsequent endeavours should focus on collaborative, equal, and ethical research.