AI in Cardiovascular Care: Population Screening Outperforms Specialized Use

AI's effectiveness varies critically based on the clinical settings; studies in high-volume, population-based contexts, such as primary care screening, showed consistent and significant diagnostic and preventive benefits. In contrast, specialized procedural contexts, like complex stress testing, showed more limited diagnostic accuracy.

This highlights AI’s strength as a robust tool for large-scale data management and risk stratification. These comparative results were published in the Journal of the American College of Cardiology: Advances (JACC Adv.).

The rapid integration of AI into medicine necessitates understanding where these tools deliver the greatest clinical utility. Given that AI thrives on standardized data analysis, a key finding from a recent systematic review of RCTs explored how AI performance differs between population-based screening (handling large, routine datasets) and specialized clinical contexts (addressing complex, tailored procedures). High-quality RCTs are essential for confirming AI’s effectiveness in real-world clinical environments.

This systematic review utilized PRISMA guidelines to identify and analyze 11 RCTs involving machine learning models in cardiovascular care. The trials were grouped based on their clinical environment; those conducted in high-volume settings (e.g., screening large cohorts for AF or low EF) and those in specialized settings (e.g., complex imaging protocols). The focus was on identifying diagnostic accuracy and subsequent clinical action.

AI interventions in high-volume, standardized settings consistently demonstrated robust diagnostic and preventive benefits. Examples include: primary care Atrial Fibrillation (AF) screening, where the use of a machine learning algorithm helped high-risk participants who underwent diagnostic testing become twice as likely to receive an arrhythmia diagnosis compared to routine care. In population-based screening for low Ejection Fraction (EF), AI alerts on ECGs resulted in a higher diagnosis rate of low EF (2.1% vs 1.6%). Furthermore, opportunistic AI screening for Coronary Artery Calcium (CAC) on CT scans led to a significant 7.4-fold increase in statin prescriptions for high-risk patients. Conversely, in specialized procedural contexts, such as AI-augmented interpretation of stress echocardiography for Coronary Artery Disease (CAD), the diagnostic accuracy proved more limited and did not meet the noninferiority margin.

Clinical Ramifications

These findings confirm that AI's immediate clinical strength lies in managing standardized, large-scale data to identify underlying risk or disease prevalence. For cardiologists, this means AI is most effective when integrated into high-volume workflows like primary care screening or opportunistic imaging analysis, serving as an early filter to flag patients who require immediate attention or preventive therapy. However, the lower performance in specialized, complex domains suggests that sophisticated interpretations still require strong human oversight or further development of tailored AI algorithms.

Reference: 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.