The article discusses the application of artificial intelligence (AI) in the detection and quantification of coronary artery calcium (CAC) from both cardiac and non-cardiac CT scans. CAC is a marker of coronary atherosclerosis and has been shown to be a powerful predictor of future cardiovascular events. The presence and severity of CAC can be accurately estimated on non-contrast chest CT scans performed for other clinical indications, but such "incidental" CAC is rarely reported. Advances in AI have enabled automatic CAC scoring for both cardiac and non-cardiac CT scans, with convolutional neural networks (CNNs) demonstrating high agreement with manual scoring. Automated CAC measurements from non-gated CT scans have the potential to improve the efficiency of healthcare systems in identifying and treating previously undiagnosed coronary artery disease. The article also highlights the advantages of AI-based CAC detection, including improved clinical efficiency, increased access to CAC scoring, and enhanced population-level data. Challenges and future directions for AI in CAC scoring are discussed, emphasizing the need for efficient workflows, cross-scanner compatibility, and the ability to distinguish between non-coronary calcifications.The article discusses the application of artificial intelligence (AI) in the detection and quantification of coronary artery calcium (CAC) from both cardiac and non-cardiac CT scans. CAC is a marker of coronary atherosclerosis and has been shown to be a powerful predictor of future cardiovascular events. The presence and severity of CAC can be accurately estimated on non-contrast chest CT scans performed for other clinical indications, but such "incidental" CAC is rarely reported. Advances in AI have enabled automatic CAC scoring for both cardiac and non-cardiac CT scans, with convolutional neural networks (CNNs) demonstrating high agreement with manual scoring. Automated CAC measurements from non-gated CT scans have the potential to improve the efficiency of healthcare systems in identifying and treating previously undiagnosed coronary artery disease. The article also highlights the advantages of AI-based CAC detection, including improved clinical efficiency, increased access to CAC scoring, and enhanced population-level data. Challenges and future directions for AI in CAC scoring are discussed, emphasizing the need for efficient workflows, cross-scanner compatibility, and the ability to distinguish between non-coronary calcifications.