Clinical Evaluation of an AI System for Streamlined Variant Interpretation in Genetic Testing

The growing use of genomic sequencing to diagnose hereditary diseases has increased the interpretive workload for clinical laboratories. Efficient methods are needed to maximize diagnostic yield without overwhelming resources. We developed DiagAI, an integrative machine-learning system trained to prioritize and sort causal variants in rare diseases. DiagAI integrates Universal Pathogenicity Predictor (UP2), a machine-learning model trained to predict ACMG pathogenicity classes, PhenoGenius to match genotype-phenotype interactions and expert features such as inheritance and variant quality. We retrospectively analyzed 196 diagnosed exomes from a nephrology cohort. To benchmark UP2s performance, we evaluated the ranking of 62 causal missense variants. UP2 ranked variants most effectively beyond shortlist sizes of 10 and identified pathogenic variants missed by AlphaMissense. DiagAI identified 94.9% of causal variants in diagnostic exomes with HPO terms, compared to 90.8% without, with median shortlist sizes of 12 and 9 variants, respectively. With HPO terms, 74% of top-ranked variants were diagnostic, versus 42% without, outperforming Exomiser and AI-MARRVEL. DiagAI produces accurate shortlists that streamline variant interpretation, offering a scalable solution for growing diagnostic volumes.