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Editorial: Contemporary applications of machine learning and artificial intelligence for the management of heart

Andre Rodrigues Duraes1, Mansueto Gomes-Neto1, Edimar Alcides Bocchi2

  • 1Federal University of Bahia (UFBA), Salvador, Brazil.

Frontiers in Cardiovascular Medicine
|June 18, 2025
PubMed

Insights

Machine learning and artificial intelligence (AI) are revolutionizing heart failure (HF) management. These advanced technologies offer new ways to diagnose, stratify, and predict outcomes for patients with HF.

Area of Science:

  • Cardiovascular Medicine
  • Biomedical Data Science
  • Computational Biology

Background:

  • Heart failure (HF) presents significant clinical and economic challenges.
  • Contemporary management of HF requires innovative approaches for diagnosis, risk stratification, and patient monitoring.

Discussion:

  • This editorial reviews four studies applying machine learning (ML) and artificial intelligence (AI) to HF.
  • The reviewed articles explore AI's role in early diagnosis via novel biomarkers, disease stratification using transcriptomics, understanding apoptotic pathways, and predictive telemonitoring.
  • Integration of these data-driven technologies into routine clinical practice is discussed, highlighting potential benefits and challenges.

Key Insights:

  • AI and ML enable early diagnosis of heart failure using novel biomarkers.
  • Transcriptomics combined with AI aids in accurate disease stratification.
  • Real-time AI models enhance predictive telemonitoring for preventing decompensation.
  • Data-driven approaches promise personalized care strategies for HF patients.

Outlook:

  • The transformative potential of AI and ML in personalizing HF care is significant.
  • Future integration of these technologies could lead to improved patient outcomes and reduced healthcare costs.
  • Addressing the challenges in implementation is crucial for realizing the full benefits of AI in HF management.

Keywords:
artificial intelligencebiomarkerscardiomyopathyhealthcare technologyheart failuremachine learningprecision medicinetelemonitoring

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