Artificial Intelligence (AI) in Cardiovascular Care: Clinical Outcomes and Operational Impact

A review of randomized trials evaluating artificial intelligence (AI) in cardiovascular care shows that while AI tools consistently deliver operational improvements across imaging and diagnostic workflows, their impact on hard clinical outcomes remains modest and far less uniform. The findings suggest that AI’s most dependable contributions at this stage lie in enhancing efficiency, reducing variability, and improving speed, rather than altering patient-level results such as mortality or major cardiovascular events.

The findings were published in September in the Journal of American College of Cardiology: Advances.

Across the studies assessed, AI-enabled interventions frequently improved workflow-related measures. In echocardiography, AI-guided acquisition helped operators obtain diagnostic-quality images more quickly and with less variability. Automation also supported more standardized measurements during interpretation. These changes reduced the time and manual effort required to complete studies, particularly among less experienced users. Despite these advantages, the improvements rarely translated into clear, consistent clinical benefits.

Trials in other domains showed a similar pattern. AI systems integrated into imaging pathways, such as those used for cardiac computed tomography, helped streamline decision-making and reduce unnecessary testing. In some cases, these tools lowered repeat imaging and reduced procedural inefficiencies. Yet measurable differences in patient outcomes, including reductions in mortality or complications, were either modest or not statistically significant.

The review found that clinical gains, when they occurred, were generally confined to narrow use cases. A small number of applications demonstrated better recognition of left ventricular dysfunction, more accurate arrhythmia diagnosis, or improvements in early identification of cardiac abnormalities. These benefits, however, were limited to specific ECG-based tools and did not extend broadly across imaging or procedural applications. As a result, the overall clinical effect of AI across cardiovascular trials remains inconsistent.

One important factor behind this pattern is that operational metrics respond quickly to automation and standardization, while clinical outcomes depend on a broader ecosystem of clinical decisions, patient behavior, and long-term management. AI may improve a diagnostic step, but altering downstream treatment decisions or long-term prognosis requires a more complex chain of changes that short- and medium-term trials are not designed to capture. Furthermore, many trials remain limited in size, reducing their ability to detect differences in relatively infrequent outcomes such as mortality or major adverse cardiovascular events.

Still, the operational improvements observed are meaningful, particularly in high-volume cardiovascular services. Faster acquisition, more reproducible interpretation, and reduced need for repeat testing can alleviate clinical workload and help streamline patient pathways. While these changes may not immediately influence hard outcomes, they can support broader efforts to improve system-level efficiency and access to timely care.

Final Insights

The final practical insight for clinicians and stakeholders at this point is grounded. Overall, the evidence demonstrates that AI’s clearest and most reliable advantages in cardiovascular medicine today lie in workflow enhancement rather than clinical transformation. Although select applications show pockets of clinical promise, the current generation of AI tools primarily strengthens efficiency and consistency. As research evolves and AI becomes more embedded in clinical decision-making, stronger clinical outcome benefits may emerge. For now, however, operational gains remain the dominant and most dependable advantage reflected across randomized cardiovascular trials.

Reference: Hadida Barzilai D, Sudri K, Goshen G, Klang E, Zimlichman E, Barbash I, Cohen Shelly M. Randomized Controlled Trials Evaluating Artificial Intelligence in Cardiovascular Care: A Systematic Review. JACC Adv. 2025 Sep 24;4(11 Pt 1):102152. doi: 10.1016/j.jacadv.2025.102152. Epub ahead of print. PMID: 40997553; PMCID: PMC12506480.