Language models reveal evidence gaps in variants of uncertain significance

Transforming unstructured variant summaries into a structured, evidence-type matrix enables scalable detection of evidence gaps, allowing for the systematic integration of new data sources, and prioritization of VUS that are most likely to be reclassified. This language model-enabled pipeline provides a generalizable digital approach to identify clinical evidence gaps as functional screens, biobank resources, and computational predictors continue to evolve.