Author(s) :

Dr. Sridhar Reddy.

Volume/Issue :

Abstract :

Early detection of rare genetic disorders in neonates is crucial for timely intervention and improved clinical outcomes. Traditional diagnostic methods face limitations including lengthy turnaround times, fragmented data, and reliance on clinician expertise, which often delay diagnosis. Artificial Intelligence (AI), through advanced machine learning and deep learning algorithms, offers a transformative approach by rapidly analyzing complex genomic, phenotypic, and clinical data to identify patterns indicative of rare diseases. Integrating AI with neonatal screening programs enhances diagnostic accuracy, reduces the diagnostic odyssey, and enables personalized care. Despite challenges such as data privacy, algorithmic bias, and ethical considerations, ongoing advancements in AI and collaborative efforts promise to revolutionize neonatal care. This article explores the role of AI in genomic and phenotypic data analysis, real-world applications, benefits, challenges, and future prospects of AI-driven early detection of rare genetic disorders in neonates.

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