EAGLE-AI: A large language model workflow for automated extraction and scoring of literature evidence linking genes to autism spectrum disorder

We previously developed the Evaluation of Autism Gene Link Evidence (EAGLE) manual curation framework and used it to characterise 219 autism-associated genes. However, this effort took years of human work. We present EAGLE-AI, an automated evidence collection, screening, extraction, and scoring system incorporating agentic large language model (LLM) workforces. On a test set of 116 manuscripts screened for ease of machine-readability, EAGLE-AI achieves F1 score of 91% in reproducing human curators’ data extractions. Its evidence scores differ from those of human scorers by 14.3%. Our findings indicate that EAGLE-AI can successfully automate most of a clinical genomics evidence curation process.