@inproceedings{pronesti-etal-2026-autoforest,
    title = "{A}uto{F}orest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis",
    author = "Pronesti, Massimiliano  and
      Miculescu, Angelo  and
      Kapdi, Mohsin  and
      Flanagan, Paul  and
      Redmond, Ois{\'i}n  and
      Bettencourt-Silva, Joao H  and
      Mannu, Gurdeep Singh  and
      Denaxas, Spiros  and
      Providencia, Rui  and
      Belz, Anya  and
      Hou, Yufang",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.13/",
    pages = "128--137",
    ISBN = "979-8-89176-392-0",
    abstract = "Systematic reviews rely on forest plots to synthesise quantitative evidence across biomedical studies, but generating them remains a fragmented and labour-intensive process. Researchers must interpret complex clinical texts, manually extract outcome data from trials, define appropriate interventions and comparators, harmonise inconsistent study designs, and carry out meta-analytic computations{---}typically using specialised software that demands structured inputs and domain expertise. While recent work has demonstrated that large language models can extract study-level data from unstructured text, no existing system automates the complete pipeline from raw documents to synthesised forest plots. To address this gap, we introduce AutoForest, the first end-to-end system that generates publication-ready forest plots directly from biomedical papers. Given one or more study papers, AutoForest automatically suggests ICO (Intervention, Comparator, Outcome) elements, extracts outcome data, performs statistical synthesis, and renders the final forest plot. We describe the system architecture, user interface and demonstrate its effectiveness on real-world examples through a user study involving clinicians, showing how AutoForest can accelerate evidence synthesis and substantially lower the barrier to conducting meta-analyses."
}