Towards trustworthy AI-driven cuffless blood pressure monitoring

Cuffless blood pressure monitoring is becoming increasingly feasible with the rise of wearable technologies, offering significant promise for preventive cardiovascular care. Yet diverse sensing modalities and modeling strategies have produced fragmented evidence that is difficult to compare. In this systematic review, we introduce a unified multi-axis taxonomy that relates surrogate signals, modeling paradigms, and calibration strategies, providing a coherent structure for the field. Building on this foundation, we analysed current approaches across physiological and wearable sensing and evaluated performance using best machine-learning practices. We highlight key barriers to real-world deployment-including protocol realism, calibration drift, population diversity, and fairness relevant reporting-and translated these findings into an actionable model-card-style reporting framework for cuffless BP studies. Our framework establishes the methodological basis for reliable, equitable, and clinically valid cuffless BP monitoring, a vital step towards democratizing continuous BP monitoring for widespread clinical benefit.