Evidence Aggregator: AI reasoning applied to rare disease diagnostics

Retrieving, reviewing, and synthesizing technical information can be time-consuming and
challenging, particularly when requiring specialized expertise, as is the case of variant assessment
for rare disease diagnostics. To address this challenge, we developed the Evidence Aggregator
(EvAgg), a generative AI tool designed for rare disease diagnosis that systematically extracts
relevant information from the scientific literature for any human gene. EvAgg provides a thorough
and current summary of observed genetic variants and their associated clinical features, enabling
rapid synthesis of evidence concerning gene-disease relationships. EvAgg demonstrates strong
benchmark performance, achieving 97% recall in identifying relevant papers, 92% recall in
detecting instances of genetic variation within those papers, and ~80% accuracy in extracting
individual case and variant-level content (e.g. zygosity, inheritance, variant type, and phenotype).
Further, EvAgg complemented the process of manual literature review by identifying a substantial
number of additional relevant pieces of information. When tested with analysts in rare disease case
analysis, EvAgg reduced review time by 34% (p-value < 0.002) and increased the number of papers,
variants, and cases evaluated per unit time. These savings have the potential to reduce diagnostic
latency and increase solve rates for challenging rare disease cases.