Artificial intelligence and digital twins for the personalised prediction of hypertension risk
Akhil Naik, Jakub Nalepa, Agata M. Wijata, J.R. Mahon, Dharmesh Mistry, Adam T Knowles, Ellen A. Dawson, Gregory Y.H. Lip, Iván Olier, Sandra Ortega‐Martorell
Abstract
Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various cardiovascular diseases. Traditional approaches to hypertension risk prediction, which rely on broad epidemiological data and common risk factors, often fail to account for individual variability, highlighting the need for advanced data-driven methodologies. This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the prediction of hypertension risk by incorporating a range of data sources, including clinical, lifestyle, and genetic factors. Despite promising developments, challenges such as data standardisation, the need for high-quality datasets, model explainability, and class imbalance in medical data persist. The integration of wearable technologies, alongside the potential of emerging technologies in healthcare such as digital twins, presents significant opportunities in personalising care through the dynamic modelling of individual health profiles. This review synthesises current methodologies, identifies existing gaps, and highlights the transformative potential of AI-driven, personalised hypertension prevention and management, emphasising the importance of addressing issues of reproducibility and transparency to facilitate clinical adoption.