EAGLE-AI: A large language model workflow for automated extraction and scoring of literature evidence linking genes to autism spectrum disorder
Furlan, V.; Moran, J.; Salazar, N. B.; Rennie, O.; Hoang, N.; wan, A.; Mendes de Aquino, M.; Engchuan, W.; Vorstman, J. A. S.; Scherer, S. W.
Show abstract
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.
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