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This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (858) Information Retrieval (367) Tools, Programs and Algorithms (428) Natural Language Processing (997) Genomics and Bioinformatics for Clinical Use (53) Digital Health Meta-Research and Bibliographic Studies (80) AI Language Models in Health Care (295)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73822, first published .
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Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using GPT-4o and PubTator 3.0

Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using GPT-4o and PubTator 3.0

Authors of this article:

Claudia Giuliani1 Author Orcid Image ;   Gita Benadi1 Author Orcid Image ;   Felix Engel1 Author Orcid Image ;   Jonas Werner1 Author Orcid Image ;   Manuel Watter1 Author Orcid Image ;   Guido Schwarzer1 Author Orcid Image ;   Olaf Groß2, 3 Author Orcid Image ;   Robert Zeiser3, 4 Author Orcid Image ;   Harald Binder1, 3 Author Orcid Image ;   Klaus Kaier1, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Kaier K, Engel F, Benadi G, Giuliani C, Watter M, Kalantari A, Schuller K, Franzke C, Sperandio M, Binder H. Identification of biomedical entities from multiple repositories using a specialized metadata schema and search-augmented large language models. BMC Research Notes 2026;19(1) View
  2. Watter M, Giuliani C, Benadi G, Engel F, Binder H, Kaier K. Automated identification of contextually relevant biomedical entities with grounded LLMs. Scientific Reports 2026;16(1) View