OBJECTIVES/GOALS: Artificial Intelligence (AI) is gaining popularity in a variety of disciplines. While clinical applications for AI have increased in recent years, the use of AI in pediatric congenital heart disease (CHD) is limited. The goal of this systematic review was to assess how AI is currently used in this patient population and to describe knowledge gaps. METHODS/STUDY POPULATION: A systematic search was performed up to July 2023 using PubMed and Scopus databases and revealed 814 articles. Upon initial screening, 161 duplicates, 76 non-AI articles, and an additional 318 irrelevant articles were removed. A total of 259 full-text articles were reviewed for relevance. Articles that did not include a retrospective or prospective review of human subject data were excluded. Articles that had only results in the adult, prenatal, or non-CHD population were excluded. The remaining 68 articles were included in this review. RESULTS/ANTICIPATED RESULTS: Of the 68 articles in this review, 19 were performed within cardiac surgery, 41 were within cardiology, and the remaining 8 included articles were combined cardiac surgery and cardiology. Upon initial review, 24 used AI for diagnostic purposes, 40 for predicting survival or adverse outcomes, 2 for developing training tools, and 2 for surveillance of CHD trends. We anticipate that upon further review of these 68 articles, there will be a wide variety in the types of AI models that were used. The results will reveal a multitude of challenges and limitations that future studies will need to further address. DISCUSSION/SIGNIFICANCE: While technical innovations in pediatric CHD have dramatically improved survival rates, we have hit a plateau in improving the complications of these patients. AI has created an opportunity to build new diagnostic, predictive, teaching, and surveillance tools for advancing CHD care, but it seems we still have a long way to go.