Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT)
Michelle C. Williams, Jonathan Weir‐McCall, Lauren A. Baldassarre, Carlo N. De Cecco, Andrew D. Choi, Damini Dey, Marc R. Dweck, Ivana Išgum, Márton Kolossváry, Jonathon Leipsic, Andrew Lin, Michael T. Lu, Manish Motwani, Koen Nieman, Leslee J. Shaw, Marly van Assen, Edward Nicol
Abstract
Artificial intelligence (AI), and machine learning (ML) in particular, are rapidly transforming the world around us. In healthcare, AI/ML has the potential to improve every point in the clinical pathway. For cardiovascular computed tomography (CT) AI/ML could aid patient selection and screening, referrals and scheduling, image acquisition and reconstruction, image analysis and diagnosis, report generation, prognostication and risk stratification, management recommendations and follow-up. However, there are important challenges that must be considered for the development, assessment, and implementation of AI/ML to ensure that it is safe, reliable, cost effective, and improves outcomes for patients.