Artificial Intelligence in Tetralogy of Fallot: From Prenatal Diagnosis to Lifelong Management: A Narrative Review

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.