Artificial intelligence offers the potential for transformational advancement in cardiovascular care delivery, yet practical applications of this technology have yet to be embedded in clinical workflows and systems. Recent advances in machine learning algorithms and accessibility to big data sources have created the ability for software to solve highly specialized problems outside of health care, such as autonomous driving, speech recognition, and game playing (chess and Go), at superhuman efficiency previously not thought possible. To date, high-order cognitive problems in cardiovascular research such as differential diagnosis, treatment options, and clinical risk stratification have been difficult to address at scale with artificial intelligence. The practical application of artificial intelligence in the underlying operational processes in the delivery of cardiac care may be more amenable where adoption has great potential to fundamentally transform care delivery while maintaining the core quality and service that our patients demand. In this article, we provide an overview on how these artificial intelligence platforms can be implemented to improve the operational delivery of care for patients with cardiovascular disease.
Keywords: artificial intelligence; echocardiography; model; tomography.