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PURPOSE: Variant assessment of rare disease diagnostics depends on using domain knowledge in the time-intensive process of retrieving, reviewing, and synthesizing clinical and technical information.METHODS: To address these challenges, we developed the Evidence Aggregator (EvAgg), an open-source, generative-AI-based tool designed to support rare disease diagnosis that systematically extracts relevant information from the scientific literature for any human gene. Further, we constructed an expert-curated dataset and evaluated EvAgg's performance for the tasks of relevant paper selection, finding observations of human genetic variation within those papers, and extracting specific details about those observations (e.g. zygosity, variant inheritance, variant type, functional study. phenotype, and study type). A user study evaluated utility and user experience in rare disease case analysis.RESULTS: Our evaluation study revealed that EvAgg achieved 92% recall in identifying relevant papers, 96% recall in detecting instances of genetic variation within those papers, and ∼80% accuracy in extracting individual case and variant-level content. Our subsequent user study evaluated the utility and user experience in rare disease case analysis. We found that EvAgg reduced review time by 34% (p-value < 0.002) and increased the number of papers, variants, and cases evaluated per unit time.CONCLUSION: EvAgg provides a thorough and current summary of observed genetic variants and their associated clinical features, supporting the process of manual literature review and enabling rapid synthesis of evidence concerning gene-disease relationships. The demonstrated time savings have the potential to reduce diagnostic latency and increase solve rates for challenging rare disease cases.