JMIR Publications

ABSTRACT

Cardiovascular biomarkers are central to diagnosis, risk stratification, treatment selection, and longitudinal monitoring, yet many promising signals fail to translate from discovery into routine care. The problem is not simply a lack of biological plausibility or predictive performance. Rather, candidate biomarkers often move forward without sufficient attention to analytical validity, external validation, calibration, clinical utility, workflow fit, equity, governance, and post-deployment monitoring. Artificial intelligence may intensify both the promise and the risk of this pathway. AI-enabled cardiovascular biomarkers can integrate signals from electrocardiograms, imaging, laboratory data, electronic health records, wearable devices, omics platforms, and longitudinal clinical trajectories. Used well, these tools may support more individualized interpretation and earlier recognition of clinically important patterns. Used poorly, they may give weak or biased evidence a false appearance of precision, allowing prediction models to enter care before they are clinically validated or held to clear accountability standards. This Viewpoint argues that prediction is not validation and that AI-enabled biomarkers should be evaluated as decision-support interventions embedded within real cardiovascular workflows, rather than as isolated technical outputs. We propose a clinician-centered framework built around three linked pillars: validation, governance, and workflow integration. Validation asks whether the biomarker or model is reliably measured, externally tested, calibrated, clinically meaningful, and useful for a defined cardiovascular decision. Governance asks who is responsible for intended use, local oversight, risk management, equity assessment, audit trails, version control, and post-deployment monitoring. Workflow integration asks whether the output reaches the right user, at the right time, in a form that supports safe, accountable action. For digital cardiovascular health systems, the central task is to move from discovery and prediction toward implementation in clinical care. AI-enabled biomarkers should earn trust through evidence, workflow fit, clinician oversight, and accountable governance before they influence patient-facing decisions.