A recent review concluded that
artificial intelligence (AI) is revolutionising cardiology by transforming
clinical practice. From automating the interpretation of ECGs and cardiac
imaging to enabling remote monitoring via smartwatches, AI is shifting
cardiovascular care from a reactive to a preventive approach. The predictive
capabilities of AI for heart failure readmission risk and drug dosing mark
significant progress towards personalised medicine.
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