ArticleComputational and structural biotechnology journal2025
Developing foundations for biomedical knowledgebases from literature using large language models - A systematic assessment.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.medRxiv : the preprint server for health sciences · 2026Article
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While large language models (LLMs) have shown promising capabilities in biomedical applications, measuring their reliability in knowledge extraction remains a challenge. We developed a benchmark to compare LLMs in 11 literature knowledge extraction tasks that are foundational to automatic knowledgebase development, with or without task-specific examples supplied. We found large variation across the LLMs' performance, depending on the level of technical specialization, difficulty of tasks, scattering of original information, and format and terminology standardization requirements. We also found that asking the LLMs to provide the source text behind their answers is useful for overcoming some key challenges, but that specifying this requirement in the prompt is difficult.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.