  Regular blood pressure (BP) monitoring in clinical and ambulatory settings
plays a crucial role in the prevention, diagnosis, treatment, and management of
cardiovascular diseases. Recently, the widespread adoption of ambulatory BP
measurement devices has been driven predominantly by the increased prevalence
of hypertension and its associated risks and clinical conditions. Recent
guidelines advocate for regular BP monitoring as part of regular clinical
visits or even at home. This increased utilization of BP measurement
technologies has brought up significant concerns, regarding the accuracy of
reported BP values across settings. In this survey, focusing mainly on
cuff-based BP monitoring technologies, we highlight how BP measurements can
demonstrate substantial biases and variances due to factors such as measurement
and device errors, demographics, and body habitus. With these inherent biases,
the development of a new generation of cuff-based BP devices which use
artificial-intelligence (AI) has significant potential. We present future
avenues where AI-assisted technologies can leverage the extensive clinical
literature on BP-related studies together with the large collections of BP
records available in electronic health records. These resources can be combined
with machine learning approaches, including deep learning and Bayesian
inference, to remove BP measurement biases and to provide individualized
BP-related cardiovascular risk indexes.
