(1) Objective: This review aims to identify the clinical and practical barriers to optimizing nutrition in newborn infants with congenital heart disease (CHD) and to describe updated evidence-based recommendations for clinical and nutritional management of these patients in a narrative review. (2) Research Methods and Procedures: We conducted a search of the relevant literature published from 2000 to December 2021. (3) Results: CHD patients undergo several nutritional challenges related to the underlying cardiac disease anomaly, the potential increased risk of NEC, and delayed enteral feeding, resulting in inadequate energy intake and sub-optimal growth, increased morbidity and mortality. (4) Conclusions: To optimize nutrition and growth in newborn infants with CHD, standardized protocols should be implemented. Regular nutritional and growth assessment with a multi-disciplinary team is essential. We propose a decisional algorithm that may represent a potentially useful tool to guide clinicians to optimize growth and nutrition.

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, with profound implications for congenital heart disease (CHD). Tetralogy of Fallot (ToF), the most common cyanotic disease, requires lifelong surveillance and complex management because of late complications such as pulmonary regurgitation, arrhythmias, and right ventricular dysfunction. This review synthesizes current evidence on AI applications across the continuum of ToF care—from prenatal diagnosis to adulthood follow-up. We examine advances in imaging, perioperative planning, intraoperative monitoring, intensive care, and long-term surveillance, including wearable and implantable technologies. Machine learning (ML), deep learning (DL), and natural language processing (NLP) are revolutionizing diagnostic accuracy, risk stratification, surgical decision-making, and personalized long-term care. The future lies in the integration of multimodal data, including imaging, electronic health records (EHRs), genomic information, and continuous monitoring, to support precision medicine. Challenges remain regarding dataset limitations, interpretability, regulatory standards, and ethical concerns. Nevertheless, ongoing innovation and collaboration between clinicians, engineers, and regulators promise a new era in congenital cardiology. By embedding AI throughout the patient journey, healthcare systems may improve outcomes and quality of life for individuals with ToF.