Purpose of Review <p>This review summarizes the role of incidentally and non-incidentally discovered coronary artery calcification (CAC) and the evolving role of non-coronary artery calcification in atherosclerotic cardiovascular disease (ASCVD) risk assessment. Additionally, this review explores the emerging use of artificial intelligence (AI), machine learning (ML), radiomics, and natural language processing (NLP) for automated detection, quantification, and communication of these incidentally discovered findings.</p> Recent Findings <p>This review summarizes recent findings in the space, including the development of various AI/ML-based approaches for automated calcification quantification and detection. Recent work leverages the use of incidentally discovered CAC and non-coronary calcification (e.g. aortic valve, aortic arch, carotid artery, breast arterial calcification) and their influence on clinical decision-making and prescribing practices.</p> Summary <p>CAC and various forms of non-coronary artery calcifications are increasingly recognized as powerful and additive predictors of ASCVD risk. Advances in AI, ML, and radiomics enable scalable, automated measurement of both incidental and non-incidental CAC and non-coronary calcifications, which will facilitate more precise, personalized ASCVD risk stratification.</p>