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Artificial Intelligence in CV Care
Artificial Intelligence in Cardiovascular Care: Its Current Role and Future Capabilities

Released: September 29, 2025

Pierre Elias
Pierre Elias, MD
Sammy Elmariah
Sammy Elmariah, MD, MPH
Francisco Lopez-Jimenez
Francisco Lopez-Jimenez, MD, MSc, MBA
Evangelos K. Oikonoumou
Evangelos K. Oikonoumou, MD, DPhil
Louise Sun
Louise Sun, MD, SM, FRCPC, FAHA
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Key Takeaways
  • Artificial intelligence (AI) in cardiovascular care is enhancing cardiovascular disease and irregular arrythmia detection, risk prediction and stratification, procedure support, and drug discovery.
  • Although AI is revolutionizing the cardiovascular care landscape, it is not meant to replace the expertise and decision-making of HCPs
  • A key challenge with AI use remains—the black box issue requires newer AI models to be transparent in how conclusions are made and next steps in patient care are determined.

Artificial intelligence (AI) is rapidly reshaping the cardiovascular care landscape, offering new opportunities to extend the precision and reach of healthcare professionals (HCPs) across diagnostics, therapeutics, and care delivery. Rather than replacing clinical judgment, AI functions best as an adjunct to HCP expertise—providing novel insight, supporting timely decisions, and improving efficiency at the bedside and within health systems at large.

AI in Cardiovascular Care
In diagnostics, AI has demonstrated clear utility in image acquisition and analysis of electrocardiography, echocardiography, cardiac CT, and cardiac MRI. Beyond automating measurements, AI models can now uncover patterns that are invisible to the human eye, such as early left ventricular dysfunction or underdiagnosed conditions like cardiac amyloidosis. Interventional cardiology has benefited from AI-enabled CT modeling, which predicts complications during valve procedures and supports optimal device selection. Similarly, CT-derived fractional flow reserve has introduced a noninvasive method to assess coronary lesions, shifting from what once required invasive testing.

Furthermore, AI is valuable at the operational level. During the COVID-19 pandemic, Dr Lousie Sun helped her institution develop predictive AI models to estimate hospital and intensive care unit length of stay and to determine which patients could be fast-tracked for surgery. This allowed her teams to conserve resources and minimize surgery cancellations during a heightened time. In addition, the experience here underscores AI’s potential in aligning clinical capacity with urgency—a common challenge that presents in strained healthcare systems.

Challenges and Limitations
Despite all the positives with using AI in cardiovascular care, its limitations are significant. Trust and explainability remain key barriers for both patients and HCPs. This is especially true regarding AI outputs that suggest novel diagnoses or inform patient care. We have found that most HCPs are less concerned with whether an AI model uses deep learning or about the details of its coding but rather care more about knowing that it is accurate, reliable, and clinically useful. Low disease prevalence compounds this challenge, as even highly accurate AI models can yield too many false positives in identifying rare diseases, thereby eroding confidence and contributing to alert fatigue.

Then there are wearable technologies that often are consumer-driven. Although smart devices offer low-cost assistance with arrhythmia detection and broad accessibility, validation remains incomplete. This is because these technologies often are used on narrow populations so their generalizability is limited. That is, people in the general population voluntarily use smartwatches and such, creating self-selection bias in the data.

Finally, the administrative burden of AI is a paradox. As it is intended to streamline clinical practice, multiple dashboards and alerts can unfortunately create complexity and add to HCP burden. We have heard from our fellow HCPs that they will ignore AI alerts, even for conditions with grave implications, which reflects the danger of over-saturation and importance of thoughtful AI design.

Best Practices
Despite these challenges, AI applications are improving cardiovascular disease detection and risk stratification. AI-enhanced cardiac imaging, predictive analytics based on electronic health record data, and wearable technologies illustrate the spectrum of current AI adoption in cardiovascular care. And the successful implementation of AI in cardiology practice requires common principles: it should respond to relevant clinical problems, involve key stakeholders from the outset, and demonstrate tangible improvements in established workflows and patient outcomes.

Of note, education is essential. Even a brief training will ensure HCPs can interpret AI outputs accurately and address patient questions. Embedding AI seamlessly into the electronic health record also is critical. This would allow the results to appear at the point of care rather than on separate platforms. Furthermore, systems must balance autonomy with safety, which might include adding safeguards, for example, so that automated referrals are made if alerts go unanswered. Finally, continuous monitoring for data drift ensures sustained validity, while vigilance for bias and representativeness of training data helps maintain the AI model’s equity and generalizability.

The Future of AI
Looking ahead, AI will likely help cardiology and nonspecialist HCPs alike in recognizing underdiagnosed disease and integrating multimodal data streams to guide personalized treatment. But technical progress alone is not enough; successful AI adoption requires cultural change. HCPs must adapt to new AI models in their practice and health systems must ensure that implementation enhances rather than fragments already established workflows.

AI should be viewed as a partner in cardiovascular care. By expanding diagnostic reach, anticipating disease/procedure trajectories, and aligning resources with patients’ needs, AI can augment HCPs practice to improve both individual patient care and health system–wide performance. Thoughtful, evidence-based integration will be key to ensuring that AI fulfills its potential to transform cardiovascular care into a more precise, efficient, and patient-centered discipline. 

Your Thoughts
Do you or your institution/health system currently use AI in cardiology practice? You can get involved in the discussion by answering the poll question and posting a comment below.


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