Work

Automated identification of contextually relevant biomedical entities with grounded LLMs

Year: 2026

Type: article

Abstract: This study investigates the effectiveness of different large language models for automated biomedical entity annotation in research articles with a focus on contextualized and grounded results. A 4-step generative workflow iteratively generates and refines entity candidates by considering a metadata schema for context and agentic tool use for validation with the PubTator 3 data base. The precision of this flow was assessed with a random effects meta-analysis after face-to-face interviews with authors of six papers from the Collaborative Research Center (CRC) 1453 “NephGen”. With an overall precision of 91.3%, the selected models provide qualitatively valuable annotations, with models GPT-4.1, GPT-4o Mini, and Gemini 2.0 Flash showing the highest precision. While GPT-4.1 and Gemini 2.0 Flash excelled in the total number of correct annotations, GPT-4o Mini and Gemini 2.0 Flash were fastest and most cost-effective. Large variations in annotation count and the conflation of publication and dataset-specific annotations highlight that human review ("human-in-the-loop") is still important. The results further highlight the trade-offs between precision, total number of correct annotations, cost, and speed. While quality is paramount in collaborative research settings, cost-effectiveness could be more critical in public implementations.

Source: Scientific Reports

DOI: 10.1038/s41598-026-35492-8

Authors: Manuel Watter, Claudia Giuliani, Gita Benadi, Felix Engel, Harald Binder, Klaus Kaier

Institutions: University of Freiburg, University Medical Center Freiburg

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: Deutsche Forschungsgemeinschaft, Albert-Ludwigs-Universität Freiburg