Generative AI Accelerates Genotype-Phenotype Characterization of a 1600-Case Leigh Syndrome Virtual Cohort from Published Literature.

Leigh Syndrome Spectrum (LSS) is a rare and heterogeneous disease continuum with most published cohorts in small sizes that limit the statistical power. Large-scale meta-analyses with published case-level clinical data extracted from the literature are essential for robust population analysis but are hindered by the burden of manually standardizing the unstructured, heterogeneous, and sparse case-level data from the literature. We developed a novel workflow which is among the first to combine Generative AI (GenAI) with human-in-the-loop curation to overcome this barrier. This pipeline utilized Google's Gemini-2.5-pro and rapidly processed over 2300 cases from published case data tables in two weeks and achieved >90% accuracy in mapping raw clinical data to Human Phenotype Ontology (HPO) terms. This process rapidly yielded a harmonized LSS virtual cohort of 1679 data-rich cases, which is the largest LSS virtual cohort reported so far, and thus enables characterization of LSS phenotypic and genetic architectures, revealing that autosomal recessive (932 cases) and mitochondrial (752 cases) inheritance are the most common. The most frequently mutated genes were