Revolutionizing Cardiac Care: Artificial Intelligence Applications in Heart Failure Management

Recent advancements in artificial intelligence (AI) have revolutionized the diagnosis, risk assessment, and treatment of heart failure (HF). AI models have demonstrated superior performance in distinguishing healthy individuals from those at risk of congestive HF by analyzing heart rate variability data. In addition, AI clinical decision support systems exhibit high concordance rates with HF experts, enhancing diagnostic precision. For HF with reduced as well as preserved ejection fraction, AI-powered algorithms help detect subtle irregularities in electrocardiograms and other related predictors. AI also aids in predicting HF risk in diabetic patients, using complex data patterns to enhance understanding and management. Moreover, AI technologies help forecast HF-related hospital admissions, enabling timely interventions to reduce readmission rates and improve patient outcomes. Continued innovation and research are crucial to address challenges related to data privacy and ethical considerations and ensure responsible implementation in healthcare.

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Publication Details

Journal
Cardiology in Review
Published
2025-01-09
DOI
https://doi.org/10.1097/crd.0000000000000851
Citations
3
Primary Topic
Blood Pressure and Hypertension Studies
Type
article
Field-Weighted Citation Impact
4.16
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article

Revolutionizing Cardiac Care: Artificial Intelligence Applications in Heart Failure Management

Areeba Fareed, Rayyan Vaid, Ayesha Sarwar, Abdulrahmon Moradeyo et al.
3 citations
Cardiology in Review
Blood Pressure and Hypertension Studies
4.16
article

Revolutionizing Cardiac Care: Artificial Intelligence Applications in Heart Failure Management

Areeba Fareed, Rayyan Vaid, Ayesha Sarwar, Abdulrahmon Moradeyo, Aashar Khalid, Afra Sohail
article en
3 citations

Abstract

Recent advancements in artificial intelligence (AI) have revolutionized the diagnosis, risk assessment, and treatment of heart failure (HF). AI models have demonstrated superior performance in distinguishing healthy individuals from those at risk of congestive HF by analyzing heart rate variability data. In addition, AI clinical decision support systems exhibit high concordance rates with HF experts, enhancing diagnostic precision. For HF with reduced as well as preserved ejection fraction, AI-powered algorithms help detect subtle irregularities in electrocardiograms and other related predictors. AI also aids in predicting HF risk in diabetic patients, using complex data patterns to enhance understanding and management. Moreover, AI technologies help forecast HF-related hospital admissions, enabling timely interventions to reduce readmission rates and improve patient outcomes. Continued innovation and research are crucial to address challenges related to data privacy and ethical considerations and ensure responsible implementation in healthcare.

Cardiology in Review
Karachi Medical and Dental College (PK), Jinnah Medical & Dental College (PK), Ladoke Akintola University of Technology (NG)
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Blood Pressure and Hypertension Studies
4.16
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Revolutionizing Cardiac Care: Artificial Intelligence Applications in Heart Failure Management — Areeba Fareed, Rayyan Vaid, et al. · Cardiology in Review (2025) | TGRS Research Map | TGRS