Artificial Intelligence in Patients with Congenital Heart Disease: Where Do We Stand?

Abstract

Life expectancy of patients with congenital heart disease (CHD) has increased in recent decades; however, late complications remain frequent and difficult to predict. Progress in data science has spurred the development of decision support systems and could aid physicians in predicting clinical deterioration and in the management of CHD patients. Newly developed artificial intelligence (AI) algorithms have shown performances comparable to humans in clinical diagnostics using statistical and computational algorithms and are expected to partly surpass human intelligence in the near future. Although much research on AI has been performed in patients with acquired heart disease, little data is available with respect to research on AI in patients with CHD. Learning algorithms in patients with CHD have shown to be promising in the interpretation of ECG, cardiac imaging, and the prediction of surgical outcome. However, current learning algorithms are not accurate enough to be implemented into daily clinical practice. Data on AI possibilities remain scarce in patients with CHD, and studies on large data sets are warranted to increase sensitivity, specificity, accuracy, and clinical relevance of these algorithms.

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