AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis
arXiv:2606.02403v1 Announce Type: new 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
The development of advanced large language models (LLMs) has matured to a point where they can be applied to complex, labor-intensive tasks in scientific research, such as evidence extraction and synthesis.
This development signifies a significant advancement in automating scientific discovery and review processes, potentially accelerating biomedical research and reducing human error and resource allocation in systematic reviews.
The fragmented and manual process of generating forest plots from biomedical studies can now be significantly automated, shifting the paradigm of systematic evidence synthesis.
- · Biomedical researchers
- · Pharmaceutical companies
- · AI/ML developers
- · Medical institutions
- · Manual data extractors
- · Traditional meta-analysis software vendors
- · Workflows reliant on extensive human review
Systematic reviews become significantly faster and more scalable, leading to more frequent updates and broader coverage of medical literature.
The increased efficiency and accuracy in synthesising evidence could lead to quicker identification of effective treatments and more robust clinical guidelines.
This automation could free up research time for more novel experimentation and hypothesis generation, accelerating the pace of biomedical innovation and drug discovery.
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Read at arXiv cs.CL