Randomized controlled trials (RCTs) confirm the substantial and practical role of Artificial Intelligence (AI) in optimizing cardiovascular procedural workflows, leading to significant gains in efficiency, resource savings, and consistency. AI-driven planning reduced unnecessary invasive procedures and improved device implantation success rates, underscoring AI’s immediate benefit as a complement to clinician expertise in high-volume settings.
These findings were detailed in a systematic review of RCTs evaluating Artificial Intelligence in Cardiovascular Care, published in JACC Advances (JACC Adv.).
The high volume and complexity of modern cardiovascular procedures require optimal planning and efficient resource management. AI offers the capability to streamline these critical operations by analyzing data and reducing human variability. This systematic review synthesized RCT evidence to quantify AI’s specific impact on procedural optimization and resource efficiency, addressing outcomes such as time savings, reduced costs, and improved procedural consistency.
The researchers reviewed 11 RCTs that evaluated machine learning models in cardiovascular care, identified using PRISMA guidelines. Three of these trials specifically demonstrated AI’s substantial role in optimizing procedural workflows. These studies assessed AI applications in echocardiography, CT-based planning, and CT-fractional flow reserve.
The results demonstrated tangible operational improvements:
• Reduced Invasive Procedures: The use of an on-site CT-fractional flow reserve strategy guided by machine learning significantly reduced the percentage of patients with stable CAD who underwent angiography without obstructive disease (28.3% vs 46.2%; P<.001).
• Procedural Planning Success: AI-enabled CT planning for transcatheter left atrial appendage closure improved patient outcomes, leading to a higher complete closure rate (61.1% vs 44.0%; P=0.03), while also improving resource efficiency by requiring 15% fewer devices and a 50% reduction in device repositioning.
• Echocardiography Consistency: AI initial interpretation of Left Ventricular Ejection Fraction (LVEF) was non-inferior to sonographer assessments and resulted in significantly fewer substantial changes between initial and final cardiologist assessments (16.8% vs 27.2%; P<.001), while saving median time for both sonographers and cardiologists.
Potential Implications for Cardiovascular Interventionalists
These findings confirm the immediate, practical benefits of implementing AI in cardiovascular procedure settings. For cardiologists, AI provides a meaningful operational advantage by reducing ambiguity in interpretation, standardizing complex planning processes, and ultimately decreasing resource waste (fewer devices, fewer unnecessary invasive tests). Although major clinical endpoints like MACE may not always be significantly impacted by these efficiency gains, the enhanced consistency, reduced clinician workload, and procedural cost savings make AI a likely essential tool for high-volume cardiovascular care.
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.
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