Deep Learning and Cardiovascular Diseases: An Updated Narrative Review

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.