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Abstract This study investigates the effectiveness of different large language models (LLMs) 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.
Watter et al. (Tue,) studied this question.