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Ischemic heart disease occurs when the heart's blood supply dwindles, causing an ominous lack of oxygen and nutrients. This deficiency, stemming from reduced or obstructed blood flow, spells danger, leading to heart muscle damage and dysfunction.
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Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.

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Artificial intelligence (AI) and deep learning (DL) are revolutionizing cardiology diagnostics and imaging. These advanced technologies enhance accuracy, efficiency, and personalized patient care in cardiovascular medicine.

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Area of Science:

  • Cardiovascular Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) and deep learning (DL) are transforming cardiovascular diagnostics and imaging.
  • These technologies offer automated segmentation, quantification, and risk stratification, improving patient care.
  • AI and DL hold potential for enhanced diagnostic accuracy and work efficiency.

Purpose of the Study:

  • To critically evaluate clinically validated applications of AI and DL in cardiovascular medicine.
  • Focus on AI/DL in cardiac imaging, risk stratification, and biomarker integration.
  • Analyze AI/DL's impact on diagnostic accuracy, efficiency, and patient care.

Main Methods:

  • Structured narrative review of AI and DL in cardiovascular medicine.
  • Systematic literature search (PubMed, Jan 2015-Dec 2026) and reference checking.
  • Analysis of 78 original English-language studies with quantitative clinical outcomes.

Main Results:

  • AI/DL models (CNNs, transformers) match expert performance in cardiac imaging and risk prediction.
  • Multimodal AI approaches improve diagnostic accuracy and reproducibility.
  • Explainable AI enhances transparency; DL enables faster image processing without precision loss.

Conclusions:

  • AI and DL offer transformative potential for fast, accurate, and scalable cardiovascular diagnostics.
  • Key factors include multimodal data integration, prospective validation, and model transparency.
  • Future research should prioritize multicenter validation and ethical AI implementation in clinical practice.