AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis

AutoForest is an AI-powered system that automates the complete pipeline for generating forest plots from biomedical research papers, eliminating the need for manual data extraction and meta-analytic synthesis. The tool uses large language models to suggest study parameters, extract outcome data, and produce publication-ready visualizations, potentially accelerating systematic reviews and lowering barriers to evidence synthesis.

AutoForest addresses a critical bottleneck in biomedical research where systematic reviews and meta-analyses require extensive manual labor to extract, harmonize, and synthesize quantitative evidence across multiple studies. Traditionally, researchers must interpret clinical texts, manually extract trial data, define study parameters, reconcile inconsistent methodologies, and employ specialized statistical software—a process that demands significant domain expertise and time investment. The introduction of an end-to-end automated system represents a meaningful advancement in research infrastructure, leveraging recent breakthroughs in large language model capabilities for structured information extraction.

This development fits within a broader trend of AI applications extending beyond narrow prediction tasks into complex knowledge work domains. The biomedical research community has increasingly adopted computational tools for document processing, but no prior system has successfully unified the entire evidence synthesis workflow. AutoForest's validation through user studies with clinicians demonstrates practical viability beyond academic proof-of-concept, suggesting genuine utility in research institutions.

The market and operational impact centers on research acceleration and democratization. Academic institutions and pharmaceutical companies conducting meta-analyses face reduced operational costs and faster timelines for evidence synthesis. More significantly, lower technical barriers enable smaller research teams and resource-constrained organizations to conduct rigorous meta-analyses previously requiring specialized expertise. This could accelerate the pace of evidence-based medicine decisions and systematic review publication rates.

Future developments to monitor include adoption rates within academic research networks, integration with existing meta-analysis platforms, and potential expansion to other evidence synthesis tasks beyond forest plots. The system's accuracy on diverse study designs and its handling of edge cases in clinical data extraction will determine long-term impact on research productivity.

• →AutoForest automates end-to-end forest plot generation from raw biomedical papers, eliminating fragmented manual processes.
• →The system uses large language models to extract study parameters, outcome data, and perform statistical synthesis automatically.
• →User studies with clinicians validate practical effectiveness and demonstrate potential to accelerate evidence synthesis workflows.
• →The tool lowers technical barriers to conducting meta-analyses, enabling smaller research teams to perform systematic reviews.
• →Integration of AI into research infrastructure represents expanding applications beyond prediction into complex knowledge synthesis tasks.