💡 AI Advances HFpEF Diagnosis and Management

Heart failure with preserved ejection fraction (HFpEF) is rising in prevalence and remains difficult to diagnose and manage, but AI and machine learning offer new approaches for earlier diagnosis, personalized care, and risk stratification.

Why It Matters To Your Practice

▪ HFpEF represents over half of all heart failure cases and is growing in burden.

▪ Current diagnostic and management tools are often inadequate in real-world settings.

▪ AI can process complex, multi-dimensional patient data beyond human capacity.

▪ Incorporating AI could streamline workflows and identify at-risk patients sooner.

Clinical Implications

▪ AI-driven models may support earlier and more accurate HFpEF diagnosis.

▪ Machine learning enables personalized phenotyping for tailored therapies.

▪ Automated risk stratification helps prioritize high-risk patients for intervention.

▪ Continuous monitoring with AI tools could improve patient outcomes.

Insights

▪ AI algorithms reveal patterns in large, heterogeneous HFpEF datasets.

▪ Current clinical adaptability is limited by data quality and integration hurdles.

▪ Interdisciplinary collaboration is key to developing effective AI tools.

▪ Future research must validate models and address real-world implementation.

The Bottom Line

▪ AI and ML have significant potential to transform HFpEF care, but practical clinical integration and validation remain critical next steps.