In a recent commentary, Dr. Michelle Kittleson, a heart failure transplant cardiologist, highlights the transformative potential of artificial intelligence (AI) in heart failure (HF) management. AI, particularly through machine learning, enables advanced pattern recognition and data-driven decision-making. It shows promise in early detection, such as predicting incident HF using electronic health records, which could prompt timely interventions like echocardiograms or peptide assessments. For diagnosis, AI-powered tools improve accuracy and reduce variability in imaging, especially for challenges like HF with preserved ejection fraction (HFpEF). AI also aids in phenotyping HFpEF patients by identifying subgroups based on clinical and imaging parameters, paving the way for personalized therapies.

Prognostication remains an area of need, as current AI models face challenges in reliability and consistency. Key barriers include balancing data complexity, ensuring privacy, and overcoming clinician skepticism. Regulatory approvals and clinician education will be critical for successful integration into practice.

Reference: Kittleson, M. From Prevention to Prognosis: AI’s Role in Heart Failure. Medscape. Published July 02, 2024. Accessed November 6, 2024. https://www.medscape.com/viewarticle/prevention-prognosis-ais-role-heart-failure-2024a1000byd?src=dpcs&form=fpf