Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review

Abstract

Abstract Objective Coronary artery calcium (CAC) scoring is an established marker of atherosclerotic burden and cardiovascular risk, yet its clinical use remains limited by access, cost and workflow constraints. Artificial intelligence-enabled CAC (AI-CAC) tools allow automated assessment of coronary calcification from chest CT performed for diverse clinical indications, creating opportunities for scalable, opportunistic cardiovascular disease risk stratification. We conducted a scoping review of AI-CAC models focused on prognostication and real-world deployment, emphasising outcomes, clinical value and implementation considerations. Methods and analysis Peer-reviewed original studies using an AI model incorporating CAC from chest CT for clinical prognostication, risk prediction or assessment of clinical interventions, reporting clinical outcomes, prognostic metrics or implementation insights. Studies focused exclusively on technical AI-CAC development or validation without clinical context were excluded. PubMed/MEDLINE, Embase and Cochrane Library, searched through November 2025, supplemented by expert recommendations from study coauthors. A single reviewer screened all records. Data were extracted using a standardised 25-variable template and synthesised narratively using an AI translational maturity framework. Results Across diverse patient populations and imaging contexts, AI-CAC demonstrated consistent associations with all-cause mortality, cardiovascular mortality, major adverse cardiovascular events and obstructive coronary artery disease. Radiomics-based approaches incorporating high-dimensional imaging features may improve prognostic performance beyond Agatston scoring alone, demonstrating further methodological potential for assessing cardiovascular risk. Emerging prospective and implementation-focused studies suggest that integrating AI-CAC into clinical workflows increases preventive care engagement. However, most evidence remains retrospective with limited evaluation of cost-effectiveness, equity or long-term patient and health system impact. Conclusion AI-CAC represents a promising approach for scaling atherosclerotic cardiovascular disease risk stratification. Beyond expanding access to traditional CAC scoring, AI-enabled approaches may support the evolution of the CAC score itself by integrating plaque characteristics, distribution and complementary imaging biomarkers to support more personalised preventive strategies rather than relying on fixed risk thresholds alone. Realising meaningful clinical impact will require prospective implementation studies, standardised reporting and evaluation, integration into care pathways and ongoing monitoring aligned with ethical, operational and financial considerations.