The current era of big data offers a wealth of new opportunities for clinicians to leverage artificial intelligence to optimize care for pediatric and adult patients with a congenital heart disease. At present, there is a significant underutilization of artificial intelligence in the clinical setting for the diagnosis, prognosis, and management of congenital heart disease patients. This document is a call to action and will describe the current state of artificial intelligence in congenital heart disease, review challenges, discuss opportunities, and focus on the top priorities of artificial intelligence–based deployment in congenital heart disease.Artificial intelligence (AI) technologies have made a major impact in imaging in cardiology and have many applications in health care delivery such as computer-assisted diagnostics, risk prediction and stratification, clinical decision support, deep phenotyping, precision medicine, and personalized prescription.

Physicians can leverage these to provide optimal care for the patients in the current era of big data. Congenital heart disease (CHD) is an excellent domain for AI given the robust and diverse data sets extending from complex disease diagnosis and management to multimodality imaging. With evolving therapies and surgeries, CHD patients are surviving longer, creating a growing population of adult CHD patients. The use of AI could help augment and optimize the management of these patients, improve quality of care, extend life expectancy, save time for the treating physician, and decrease health care costs. However, there is a significant gap in the application of AI for diagnosis, prognosis, and management of CHD patients across their lifespan. The use of AI in pediatric and adult CHD has been limited by insufficient CHD-specific labeled data sets available for training of models, complex modeling needs in this patient population due to heterogenous clinical phenotypes and age-related pathophysiological changes, and siloed data in center-specific data warehouses. Additionally, at baseline, data for specific rare forms of CHD are limited, requiring multicentered collaboration to accrue sufficient data sets. Lastly, significant deficits in clinical training, knowledge, experience, and comfort with AI exist.

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Mishita
Jornal co-ordinator
Journal of Heart and Cardiovascular Research