Artificial Intelligence and Heart Failure: A State-of-the-Art Review
Why the study?
Heart failure management faces persistent challenges, including residual risk despite therapy, phenotyping HFpEF, and integrating large volumes of disparate data for risk stratification, where AI algorithms may improve clinical decision-making.
Design
State-of-the-art review
Authors
Discussion
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Member takes
Implication
Should not yet alter HF practice; leaves open the need for prospective validation trials.
Key Points
- To review the current landscape of artificial intelligence algorithms applied to heart failure care, focusing on diagnosis, disease phenotyping, risk stratification, and practical barriers to clinical integration.
- Synthesized evidence on artificial intelligence and machine learning applications across the spectrum of heart failure management.
- Evaluated algorithm capabilities in integrating multidimensional clinical data for subclinical detection, phenotyping heart failure with preserved ejection fraction, and predicting adverse outcomes.
- Artificial intelligence models demonstrate superior predictive accuracy compared with conventional clinical risk scores for risk stratification and adverse outcome forecasting.
- Machine learning approaches facilitate the identification of subclinical disease and novel phenotypic subcategories of heart failure with preserved ejection fraction.
- Successful clinical adoption requires resolving significant challenges in real-world deployment, data interoperability, and model generalizability across diverse patient populations.
Cite This Study
Khan et al. (2023) studied this question.