ARTIFICIAL INTELLIGENCE (AI) is reshaping cardiovascular medicine by improving diagnostic precision, risk stratification, and clinical decision-making, according to a new editorial published in Open Heart. The editorial examines emerging applications of AI in cardiovascular care, highlighting research that could help bridge the gap between algorithm development and clinical practice.
AI Enhances Cardiovascular Diagnostics
AI-based approaches are expanding the capabilities of established diagnostic tools, enabling clinicians to identify abnormalities that may be difficult to detect through conventional assessment.
Research highlighted in the editorial demonstrated how deep learning could transform chest X-rays into screening tools for mitral stenosis, potentially improving access to cardiovascular diagnostics.
<a href="https://myappsplus.com/the-missing-layer-in-enterprise-ai-a-governance-architecture-for-llms-and-machine-learning/" title="The Missing Layer in Enterprise AI: A Governance Architecture for LLMs and Machine Learning“>Machine learning applied to coronary CT imaging also enabled identification of high-risk plaque characteristics that traditional visual assessment may overlook, offering opportunities for more precise myocardial infarction risk prediction.
Another emerging application involved generative AI in coronary angiography interpretation. Researchers compared ChatGPT-4o with experienced interventional cardiologists, exploring the potential of large language models to support complex clinical assessments.
Additional studies investigated radiomics and pulse wave analysis for evaluating atrial fibrillation recurrence risk and central haemodynamics without invasive procedures.
Machine Learning Improves Cardiovascular Risk Prediction
Beyond diagnosis, machine learning is advancing cardiovascular risk stratification by moving beyond conventional population-based scoring systems.
The editorial highlighted gradient-boosting algorithms, including XGBoost and CatBoost, which were investigated for guiding coronary imaging decisions and estimating 30-day risks following non-cardiac surgery.
Importantly, explainable AI frameworks, including Shapley additive explanations, were used to clarify the factors influencing individual risk predictions, addressing concerns about the interpretability of complex algorithms.
Unsupervised clustering approaches were also explored for heart failure phenotyping, potentially enabling more individualised assessment beyond traditional ejection fraction classifications.
Evidence Supports Future Clinical Integration
Systematic reviews and meta-analyses examining AI-based atrial fibrillation detection and cardiac CT demonstrated the growing evidence base supporting AI-assisted cardiovascular diagnostics.
However, the editorial emphasised that successful clinical implementation requires integration into existing workflows while maintaining clinician oversight.
The authors concluded that the next stage of cardiovascular innovation should focus on translating increasingly sophisticated AI models into practical, interpretable tools that support clinical decisions and more personalised patient care.
Tang Y et al. Navigating the intelligence revolution: transforming cardiovascular care through AI-driven innovation. Open Heart. 2026;13(1):e004160.
