LLM-Assisted Reanalysis of Unsolved Rare Disease Genomes Increases Diagnostic Yield

BACKGROUND: Rare and undiagnosed genetic disorders affect millions of patients globally, and many patients endure years of inconclusive testing. Conventional genomic interpretation can be insufficiently sensitive and costly and is rarely repeated as knowledge evolves. METHODS: We conducted a retrospective multicohort reanalysis using a large language model (LLM)-assisted workflow that ingests clinician notes, Human Phenotype Ontology (HPO) terms, and a filtered variant table to propose explanation-rich candidate hypotheses for expert adjudication under American College of Medical Genetics and Genomics and Association for Molecular Pathology criteria. A diagnosis was defined a priori as a variant classified as pathogenic or likely pathogenic, confirmed in a Clinical Laboratory Improvement Amendments-certified laboratory, and returned to families. Secondary outputs included "rediscoveries" of externally established diagnoses not yet available locally and hypothesis generation signals. RESULTS: ) and vitiligo. CONCLUSIONS: In retrospective reanalysis, an explanation-first LLM applied to routine HPO terms and variant tables produced clinically relevant gains in diagnostic yield, surfaced overlooked pathogenic findings, and generated biologically grounded hypotheses. These results motivate prospective multicenter evaluation with predefined end points, calibration reporting, and comparator baselines. (Funded by the U.S. National Institute of Child Health and Human Development and others.).