ai-driven variant prioritization in rare genetic disorders: enhancing diagnostic accuracy and clinical outcomes

Next-generation sequencing (ngs) has transformed rare genetic disorder diagnosis, but the vast genomic data poses challenges. artificial intelligence (ai) algorithms, particularly machine learning and deep learning models, enhance genetic understanding. ai algorithms can process large-scale genomic information rapidly, enabling the identification of rare variants contributing to specific rare diseases based on their predicted impact on protein function, evolutionary conservation, or known disease associations.. integrating data sources, ai aids in interpreting ngs data, uncovering genetic mechanisms. by using ai in ngs analysis, clinicians improve diagnostic accuracy and efficiency for rare genetic disorders, enabling early and precise diagnoses and personalized treatment strategies. ai-driven results outperform manual analysis in accuracy, speed, complexity, scalability, and interpretability. ai and open-source tools like exomiser excel in genetic data analysis, boosting the identification of disease-causing genetic variations. combining ai and manual analysis strengths enhances clinical diagnoses in rare genetic disorders.