The Future of Pediatric Care: AI and ML as Catalysts for Change in Genetic Syndrome Management

Abstract

This review explores the significant impact of Artificial Intelligence (AI) and Machine Learning (ML) on pediatric healthcare and education for children with genetic syndromes. Our investigation shows that AI-driven tools, like Google AI's DeepVariant, have greatly improved diagnostic precision. This allows for earlier and more accurate identification of genetic anomalies in conditions such as Cri-du-Chat Syndrome and 22q11.2 Deletion Syndrome. In addition, ML-based approaches have played a crucial role in advancing personalized treatment strategies, such as utilizing pharmacogenomic models to optimize drug therapies for Duchenne Muscular Dystrophy. Adaptive learning platforms, such as DreamBox Learning, have effectively tailored educational content according to the specific requirements of children with syndromes like Phelan-McDermid Syndrome. The review suggests that combining AI and ML significantly enhances diagnostic accuracy, treatment effectiveness, and educational results, thereby establishing higher benchmarks for pediatric care. Nevertheless, these advancements have notable ethical, legal, and social challenges. It is essential to prioritize equitable access, data privacy protection, and algorithmic transparency to maximize the benefits and minimize potential risks associated with AI. Overall, the findings underscore the potential of AI and ML to revolutionize pediatric genetic care, provided that these technologies are implemented responsibly and inclusively.

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