Towards trustworthy AI-driven cuffless blood pressure monitoring

Systematic review of 86 studies, with a multi-axis taxonomy linking surrogate signals, modelling paradigm and calibration strategy; Table 2 tabulates data source, modality, device location, n, reference BP device, validation strategy and whether the method is calibration-free. Gap analysis on protocol realism, calibration drift, population diversity and fairness reporting, and on ML-specific questions: whether validation is subject-independent and whether calibration-free vs calibration-based methods are evaluated under appropriate assumptions.

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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.

Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0

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