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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
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Artificial intelligence in heart failure - a comprehensive literature review
1Internal Medicine Hospitalist, Winchester Medical Center, Winchester, VA, United States. alyacoubramiz@yahoo.com.
Cardiology Journal
|October 14, 2025
Summary
Artificial intelligence (AI) is revolutionizing heart failure (HF) management through improved diagnosis, personalized treatments, and remote monitoring. Overcoming data and training challenges is key to fully leveraging AI for better patient outcomes and reduced healthcare burdens.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Heart failure (HF) presents significant global health challenges, marked by high hospitalization and mortality rates.
- Increasingly aging populations and HF prevalence exacerbate healthcare burdens.
- Current HF management faces limitations due to disease heterogeneity and comorbidities.
Purpose of the Study:
- To review the transformative applications of artificial intelligence (AI) in heart failure management.
- To highlight AI's potential in enhancing diagnosis, risk stratification, and personalized treatment strategies.
- To discuss barriers to AI adoption and propose solutions for its effective integration.
Main Methods:
- Systematic review of current research on AI applications in HF.
- Analysis of AI tools for diagnostic accuracy (echocardiogram, electrocardiogram).
- Evaluation of AI algorithms for predictive analytics and treatment tailoring using diverse data sources.
Main Results:
- AI improves diagnostic accuracy and identifies patient subgroups for targeted therapies.
- AI algorithms integrate multi-modal data for predicting exacerbations and personalizing treatment.
- AI-driven Clinical Decision Support Systems (AI-CDSS) enhance adherence to guideline-directed medical therapy (GDMT).
Conclusions:
- AI offers significant potential to improve heart failure patient outcomes, reduce hospitalizations, and alleviate healthcare system strain.
- Barriers including data integrity, ethical concerns, and clinician training must be addressed.
- Federated learning and interdisciplinary collaboration are crucial for overcoming challenges and realizing AI's full benefits in HF care.
Keywords:
Clinical Decision Support Systemsartificial intelligencedigital healthheart failuremachine learningtelemedicineMore Related Videos
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