AI heart monitors hit a hidden wall: accuracy isn't enough for hospitals
A new analysis reveals that even accurate AI models predicting heart health from wearable devices can't be trusted in clinical settings without better transparency and accountability safeguards. The finding threatens to slow adoption of AI-driven cardiac monitoring in hospitals and raises questions about liability when black-box algorithms guide patient care decisions.
Originaltitel: Beyond the black box: interpretability, accountability, and responsible clinical integration of AI-driven heart rate variability models—a narrative review
Background Heart 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 for black-box models used in high-stakes settings. Methods This narrative, concept-driven review examined conceptual, methodological, clinical, and governance dimensions of interpretability in AI-driven HRV prediction. A structured literature search was performed in PubMed/MEDLINE, Scopus, and Embase databases. Results The analysis showed that interpretability is not a binary property but varies by model design and deployment context. Post-hoc explainability methods may increase transparency, yet they can also be unstable, incomplete, or misleading, with potential to increase automation bias. Clinical adoption is further limited by signal-quality variability (ECG vs. wearable PPG), insufficient external validation, workflow misalignment, and unclear medico-legal responsibility. A four-step pragmatic implementation framework is proposed: data governance and signal integrity; robust model development and validation; workflow-compatible clinical integration with human oversight; and continuous post-deployment monitoring and governance. Conclusion HRV-AI systems should be treated as socio-technical interventions. Responsible adoption requires proportional transparency, explicit accountability structures, and lifecycle governance beyond predictive accuracy alone.