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Accelerating metadata annotation in collaborative research centers: A hybrid AI workflow for biomedical entities.
Bioinfo. Adv. 6:vbag283 (2026)
Background: Collaborative Research Centers rely on FAIR-compliant, richly structured metadata, yetmanual annotation is a major bottleneck. We implemented a search-augmented large languagemodel (LLM) workflow within a local research data management system to pre-annotate biomedicalentities, using human-in-the-loop verification to ensure data quality.Methods: The pipeline uses Gemini 3 Pro in a two-step prompting strategy: (1) identify datasetdeposits and stable identifiers in articles converted to Markdown; (2) extract structured fields fromcurated repository landing pages rendered via a headless browser. To handle a highly hierarchicalmetadata schema, we flattened the schema for prompting and remapped outputs to strict JSON withgranular provenance tags. Authors received pre-filled metadata and could accept, edit, or deleteentries (TP, FP, FN mapping). Performance metrics (precision, recall, F1) were estimated asproportions and synthesized via random-effects meta-analysis.Results: Among 51 screened articles, the LLM identified a repository deposit in 31; authorsresponded for 17 (55% of articles with an identified deposit; 33% of all screened articles), yielding 39verified datasets. False positives were rare (mean 0.23) and false negatives low (mean 1.46).Precision was consistently high at 99.65% (95% CI 98.42%–100.00%). Recall showed morevariability at 93.75% (95% CI 89.79%–96.96%), and the F1 score reached 96.17% (95% CI 93.78%–98.11%).Conclusions: The hybrid workflow achieved high author-verified precision with moderate recall, shiftingeffort from drafting to reviewing while maintaining schema compliance. As these metrics rely on authoracceptance and response rates were modest, broader, multi-site validation is needed.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Metadata ; Workflow ; Annotation ; Identifier ; False Positive Paradox ; Schema (genetic Algorithms) ; Json ; False Positives And False Negatives
ISSN (print) / ISBN
2635-0041
e-ISSN
2635-0041
Journal
Bioinformatics Advances
Quellenangaben
Volume: 6,
Issue: 1,
Article Number: vbag283
Publisher
Oxford University Press
Reviewing status
Peer reviewed
Institute(s)
Strategy and Digitalization (DIG)