Artificial intelligence-assisted clinical exome sequencing: Insights and outcomes from 822 pediatric diagnoses.

PURPOSE: This retrospective study examined the clinical and genetic characteristics of pediatric patients undergoing clinical exome sequencing (ES) and evaluated the performance of a commercially available artificial intelligence (AI) platform that was integrated into our analysis pipeline. METHODS: ES was performed in 822 consecutive patients at a single clinical laboratory. AI-based tools were used to jointly assess genetic information and the proband's Human Phenotype Ontology terms to support variant prioritization during the initial case review. RESULTS: A definitive molecular diagnosis was established in 22% (181 of 822) of index cases, while 40% (325 of 822) had variants of uncertain significance. Among those with a definitive diagnosis, 93% (168 of 181) had a single finding and 7% (13 of 181) had multiple findings. Of the 152 reported pathogenic/likely pathogenic variants in the fully resolved cases, 98.7% were successfully flagged by AI, and 75.0% ranked among the top 10 "most likely" variants. CONCLUSION: Clinical ES provides a substantial diagnostic yield in complex pediatric disorders. Integration of AI-powered platforms can accelerate phenotype-driven variant prioritization and facilitate rare disease diagnostics, but underscores the need for careful validation and optimization in clinical workflows.