Artificial Intelligence in Cardiovascular Autonomic Neuromodulation Evidence Boundaries and Responsible Clinical Translation

Cardiovascular autonomic neuromodulation brings together physiological treatment, behavioral support, and emerging computational technologies, but evidence for a treatment should not be mistaken for evidence that artificial intelligence improves it. This critical narrative review examines selected clinical trials and foundational neurotechnology studies alongside ethical and governance guidance. It distinguishes intervention efficacy, the incremental contribution of algorithmic adaptation, and the conditions for responsible implementation. Renal denervation has sham-controlled evidence for blood pressure reduction in selected populations; auricular stimulation and heart rate variability biofeedback have encouraging but more limited clinical evidence. Baroreflex activation requires qualified interpretation because a pivotal hypertension trial did not meet all prespecified primary endpoints. Cortical interfaces, decoded neurofeedback, and cardiac digital twins provide technological or conceptual context without establishing cardiovascular benefit from autonomous algorithmic control. No systematic search, pooled analysis, or formal certainty grading is claimed. The proposed translational framework requires comparison against competent nonadaptive care, clinically justified control limits, interpretable escalation, documented software changes, and meaningful patient participation. It also addresses mental privacy, equitable access, professional responsibility, and the organizational costs of monitoring. Artificial intelligence should therefore be evaluated as a potentially useful adjunct whose clinical and social value must be demonstrated for each intended use, rather than inferred from physiological plausibility or technological sophistication.