@article{TP155983,
author = {Jeevan Francis and Asmita Singhania and Sarah Dawson and Massimo Caputo and Jelena Savovic and Serban Stoica},
title = {Artificial intelligence in congenital heart surgery: a scoping review and primer for surgeons},
journal = {Translational Pediatrics},
volume = {15},
number = {6},
year = {2026},
keywords = {},
abstract = {Background: Congenital heart surgery (CHS) encompasses a wide spectrum of complex cardiac defects, many of which demand specialised perioperative management and tailored surgical planning. Artificial intelligence (AI), including machine learning (ML), is gaining prominence as a tool to optimise clinical decision-making and achieve better outcomes. This scoping review aims to map and summarise the existing applications of AI modalities in CHS.Methods: A comprehensive search of MEDLINE, Embase, and Web of Science was performed, combining terms for AI with terms for congenital heart disease and surgery.Results: A total of 2,871 articles were retrieved from the search, of which 93 studies were included. The majority of studies focused on outcome prediction and imaging-based applications. Smaller proportions addressed decision-making and data augmentation, omics integration, benchmarking and quality improvement. The majority of studies examined heterogeneous congenital heart disease (CHD) populations, with tetralogy of Fallot (TOF) and single ventricle physiology most frequently represented.Conclusions: AI applications in CHS are rapidly expanding across diverse domains, with early studies showing encouraging potential to support diagnostics, guide surgical decision-making, and improve perioperative outcomes. However, most models remain in the preliminary stage with limited external validation. Emerging advances in AI may further accelerate progress, but careful evaluation and integration are essential to translate this promise into tangible clinical benefits.},
issn = {2224-4344}, url = {https://tp.amegroups.org/article/view/155983}
}