Narrative review on artificial intelligence in regenerative pediatric and congenital heart surgery: clinical applications and the path to responsible innovation

Abstract

Artificial intelligence (AI) is expected to transform pediatric and congenital cardiac surgery by enhancing diagnosis, refining risk assessment, optimising perioperative planning, and supporting clinical decision-making. In congenital heart defects, the complexity and diversity of conditions, combined with the scarcity of specialist expertise, create a strong case for AI integration in regeneration and repair. Advances in machine learning, deep learning, and large language models offer the potential to analyze multimodal clinical and imaging data at scale, identify patterns beyond human detection, and deliver timely, personalized recommendations. Early applications in imaging, telemedicine, and decision support demonstrate promising performance, particularly in improving diagnostic consistency and prioritising high-risk patients for intervention. However, most artificial intelligence tools remain at the prototype stage, with limited multi-centre validation and challenges in generalisability, interpretability, and data quality. Ethical and governance frameworks, such as the FUTURE-AI consensus, emphasize fairness, transparency, and robustness as essential for safe deployment, especially in vulnerable pediatric populations. This review outlines current and emerging applications of AI in the care and repair of pediatric and congenital heart defects, highlights opportunities for improving outcomes and equity, and discusses the critical steps needed for responsible, clinician-led integration of artificial intelligence into surgical and perioperative practice. Key Words: artificial intelligence; clinical decision support; congenital heart defects; deep learning; large language models; machine learning; pediatric cardiac surgery; regenerative cardiac surgery; regenerative medicine; surgical repair

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