Screening of heart failure (HF) remains suboptimal. However, vocal biomarkers are an emerging tool to screen for HF and predict adverse outcomes. This article is a literature review of the current evidence, exploring the use of vocal biomarkers
to screen for HF and predict hospitalisation and mortality. Voice may be utilised as a time-efficient method for the screening of HF, risk stratification, and monitoring of disease progression. Vocal biomarkers such as pause ratio and artificial neural networks can screen for HF accurately in a case-control setting. Other vocal markers like maximum phonation time, creak percent are useful to monitor disease progression. Vocal biomarkers appear to match current predictors of one-year mortality, HF related decompensation and hospitalisation. The integration of vocal biomarkers assessment in HF healthcare is promising across primary and secondary care settings. Key challenges including health data protection,
regulatory compliance, and user acceptance must be addressed before these tools can be fully integrated into HF clinical care pathway.
Key messages What is known Heart failure (HF) remains under-diagnosed, particularly in early stages and community settings, despite guideline-directed diagnostic tools. Voice features and machine-learning–derived vocal biomarkers differ between individuals with HF compared to healthy controls and may be associated with hospitalisation and mortality, but studies are currently limited.
What this review adds Synthesised evidence supporting voice-based biomarkers as a non-invasive diagnostic approach for heart failure screening, risk stratification, and diagnosis. A physiological framework has been provided linking HF pathophysiology to measurable voice changes and highlights the potential of voice analysis for scalable telemedicine, while addressing key methodological, ethical, and regulatory challenges.