JMIR Publications

ABSTRACT

Artificial-intelligence clinical decision support systems are proliferating across cardiovascular medicine, yet few validated models reach routine care. The binding constraint is no longer accuracy but the absence of an auditable procedure for deciding whether a tool is fit to be deployed, kept, or withdrawn — and for protecting the patients least represented in its training data. We synthesize current validation approaches, characterize the technical, workflow, regulatory, economic, and equity barriers to implementation, and propose an operational framework in which demographic fairness is a condition of deployment rather than an afterthought. Prospective and external validation remain scarce, and reporting is dominated by discrimination metrics, with calibration and subgroup performance under-reported. A few completed pragmatic trials — artificial-intelligence electrocardiography screening for low ejection fraction, and an alert that reduced all-cause mortality — show that well-implemented tools can change hard outcomes. Implementation is impeded by data heterogeneity, model drift, limited interoperability, regulation designed for static devices, immature reimbursement, and demographic performance disparities that constitute genuine patient-safety concerns. We propose the Equity-anchored Qualification and Implementation Pathway for Cardiovascular artificial intelligence, a five-phase pathway in which each phase carries an entry requirement, an equity gate, a binary go/no-go decision, and predefined stop-and-deimplementation triggers, with a stakeholder matrix and two decision aids that make these principles auditable. Realizing the promise of cardiovascular artificial intelligence requires prospective validation, equitable deployment, and lifelong monitoring. Fairness is not the last consideration but a recurring gate.