Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges.

Abstract

Purpose of review: To define the emerging role of artificial intelligence-enhanced electrocardiography (AI-ECG) in advancing population-level screening for atherosclerotic cardiovascular disease (ASCVD), we provide a comprehensive overview of its role in predicting major adverse cardiovascular events and detecting subclinical coronary artery disease. We also outline the clinical, methodological, and implementation challenges that must be addressed for widespread adoption.

Recent findings: State-of-the-art AI-ECG models exhibit high accuracy, correctly re-classifying patients deemed 'low risk' by traditional risk models. They also compress the prediction horizon from a decade to just a few years, suggesting opportunities for early detection and more personalized intervention. However, validation remains largely retrospective and hospital-based, with referral and ascertainment biases limiting generalizability. There is no evidence thus far for an externally validated AI-ECG model that can either define or improve the detection of ASCVD outcomes independent of standard risk factors. AI-enhanced ECG interpretation has the potential to transform a universal, inexpensive test into a powerful screening and prognostication tool for ASCVD. Realizing this potential will require prospective studies to confirm that AI-ECG-guided ASCVD screening improves patient outcomes across diverse populations. Earning trust among physicians and patients will require addressing key logistical challenges, including robust data governance, seamless workflow integration, and ongoing performance monitoring. Technological innovation, such as algorithms for single-lead ECGs on wearable and portable devices, could help enable the scalability needed for global impact on cardiovascular health.