The role of artificial intelligence in hypertension management

Hypertension remains a leading modifiable risk factor for cardiovascular and renal conditions and dementia. Given its rising global prevalence and economic burden, artificial intelligence offers promising solutions across the care continuum, from diagnosis to monitoring. This review highlights recent advances in artificial intelligence-driven diagnosis and monitoring, risk stratification, and predictive modelling of hypertension-related outcomes.

Using artificial intelligence-based technologies, validated wearable cuffless monitors are developed, which use electrocardiography, heart sounds, and thoracic impedance data and provide continuous blood pressure (BP) monitoring. Artificial intelligence-generated algorithm have shown promising response to accurately predict BP. The Extreme Gradient Boost has consistently performed as the best algorithms. Additionally, these models have been used in predicting hypertension impact on cardiovascular, renal, and retinal conditions, and in predicting treatment strategies. Emerging applications of Large Language Models are being developed to provide personalized care based on individual patient characteristics.

Artificial intelligence has the potential to transform hypertension management through improved diagnosis, monitoring, and personalized care and prediction of its systemic consequences. However, challenges of model validation, interpretability, generalizability, and ethics persist. Robust prospective trials and equitable implementation strategies can help realise the potential of artificial intelligence in improving hypertension outcomes.