An artificial intelligence prediction model for optimizing patient selection for cardiac imaging for the investigation of suspected coronary artery disease

Use of the model could result in an absolute reduction of 27% in the proportion of ICAs that result in a diagnosis of normal/non-obstructive disease. This could contribute to a reduction in complications from ICA and more efficient utilization of cardiac catheterization lab capacity for higher-value cardiac interventions such as revascularization and structural procedures. Additionally, use of the model would create significant efficiencies for payors, given the much lower cost of CCTA compared with ICA. If implemented within clinical practice, the model has the potential to improve the patient experience and reduce existing health inequities.