Artificial Intelligence (AI) in Cardiovascular Care: Is It a Paradigm Shift? A recent Cureus Review

The study highlights that AI remains a powerful tool to enhance standards of cardiac care rather than to replace cardiologists.

This narrative review was published in December 2025 in Cureus Journal

The influence of AI in the field of cardiology

Artificial intelligence (AI) encompasses computational methods that identify patterns and generate predictions from complex datasets, enabling the analysis of large, multidimensional datasets and the identification of subtle, clinically relevant signals. In cardiology, this capability is being harnessed to enhance all phases of patient management, from initial diagnosis to long-term prognosis.

Authors have highlighted that, compared with numerous traditional statistical methods, contemporary machine learning techniques can capture nonlinear relationships and high-dimensional interactions, thereby revealing patterns that may elude conventional analysis. This paradigm shift has the potential to transform cardiology from a reactive, population-centred approach to a proactive, highly individualised one, ultimately leading to earlier disease diagnosis, improved treatments, and better patient outcomes.

Advanced Diagnostics

• AI-powered Electrocardiogram (ECG) analysis: AI can recognize subtle patterns in normal 12-lead ECGs that are not visible to the naked eye. This includes detecting AFib in normal sinus rhythm, offering the potential for opportunistic screening for this major stroke risk factor using a simple, readily available test.
• AI in cardiac imaging: AI is revolutionizing cardiac imaging by automating the interpretation of medical images. The authors highlighted a Mayo Clinic study that demonstrated a deep learning model that quantifies left ventricular ejection fraction (LVEF) from echocardiograms. The model's measurements matched expert assessments and performed analyses in seconds rather than minutes, proving valuable clinically.

Comparing AI vs. Traditional Methods

• Speed and efficiency: AI algorithms can process thousands of images or ECGs while experts read one, resulting in an enormous reduction in workflow bottlenecks.
• Reduced variability: Unlike human interpreters, who show varying interpretations, AI models provide objective measurements with reduced inter-observer variability.
• New discoveries: AI can recognize patterns and anticipate states beyond traditional diagnostic methods, offering new pathways for early intervention and prevention.

Predictive Analytics and Precision Medicine

• Predicting heart failure readmission: An AI model predicts 30-day readmission risk for patients with heart failure by analysing electronic health record (EHR) data (laboratory results, medications, and comorbidities) and clinician notes. The model outperformed conventional risk scores, such as the LACE score (length of stay, acuity of admission, Comorbidity, and Emergency department (ED) use). AI's ability to process diverse data enables the precise identification of at-risk patients, allowing targeted interventions to reduce readmissions.
• Personalized warfarin dosing: A machine learning model predicts optimal warfarin doses using clinical variables (age, weight, height) and genetic data (CYP2C9 and VKORC1 genotypes). The model improved outcomes and reduced adverse events compared with conventional dosing, demonstrating AI's evolution of AI from a diagnostic tool to a clinical decision-making partner.

The Growing Use of Wearable Devices in Cardiovascular Monitoring

Wearable devices, particularly smartwatches, have enabled real-time cardiovascular monitoring through enhanced sensors such as photoplethysmography (PPG) and ECG. The authors highlighted the Apple Heart Study, which included 419,297 participants, and demonstrated AFib detection using smartwatches. Participants receiving irregular pulse notifications were fitted with ECG patches for validation. Among the analyzable ECGs, 34% were confirmed to have AFib, with a positive predictive value of 0.84. Wearable devices combined with AI algorithms can predict cardiovascular risk and the development of hypertension.

Telemedicine and Remote Patient Monitoring: Broadening AI's Impact

The authors also opined that AI is central to telemedicine and remote monitoring, enabling the collection of patient health data at home and the analysis of these data to identify trends. Evidence shows that the remote monitoring of heart failure patients' weight, symptoms, and activity helps identify early signs of deterioration. Research has demonstrated the effectiveness of AI-driven remote monitoring in hypertension management. Daily blood pressure tracking reduces mortality rates by identifying elevated readings and poor medication adherence, thereby improving outcomes. AI–telemedicine integration enables proactive care by analysing patient data to identify high-risk cases and deliver personalised care, benefiting underserved populations.

Clinical Implications

Integrating AI into clinical workflows could enhance cardiovascular care. While AI excels at data processing, human oversight is still crucial. The best approach for future cardiovascular treatment might involve AI systems that assist rather than replace clinicians. This integration can lower cognitive workload, reduce diagnostic inconsistencies, and improve diagnostic efficiency. However, to achieve this, structured physician training, ongoing algorithm reviews, and adaptive clinical protocols are essential to ensure AI suggestions are correctly interpreted within the appropriate clinical context.

Reference: Mikeladze B, Nikolaishvili G, Kobaladze N. Artificial Intelligence in Cardiology: The Current Applications and Future Directions. Cureus 17(12): e99270. Published December 15, 2025. DOI 10.7759/cureus.99270