Beyond the black box: interpretability, accountability, and responsible clinical integration of AI-driven heart rate variability models—a narrative review
Crischentian Brinza
BackgroundHeart rate variability (HRV) is a widely used digital biomarker reflecting autonomic regulation and has been associated with diverse cardiovascular, critical care, and stress-related outcomes. In parallel, AI and machine-learning methods have expanded rapidly in HRV-based prediction, often achieving strong predictive performance. However, clinical translation remains constrained by limited interpretability, unclear accountability, and challenges in workflow integration, particularly fo