Dataset title: A Decade of AI-Based Synthetic Data in Biomedicine: annotated PubMed corpus (2015-2025), LLM-assisted facet annotations and citation analysis Contact person (deposit and data curation): Gorka Epelde | Strategy and Impact Department, Biogipuzkoa Health Research Institute, San Sebastian, Spain | gorka.epeldeunanue@bio-gipuzkoa.eus | ORCID: 0000-0002-5179-415X Project description: This dataset supports a systematic literature review of AI-based synthetic data generation in biomedicine over the decade 2015-2025. The study maps the methodological landscape, its temporal evolution, and patterns of adoption, citation impact and practical translation, based on a corpus of 4,143 PubMed records retrieved with a structured Boolean query targeting the intersection of synthetic/augmented data, biomedical domains, and AI/ML technologies. Beyond the landscape findings, the study contributes a methodology for scaling systematic reviews through LLM-assisted annotation validated against human expert consensus. Six independent expert annotators established ground truth on successive batches of papers, iteratively refining the labelling guidelines; multiple LLMs were then used as semi-automated annotators and evaluated against that ground truth before being applied to the full corpus. Data description: The deposit contains four groups of tabular files in .xlsx format, plus the analysis code: (a) The source corpus of 4,143 PubMed records -> csv-SyntheticD-set2015-April2025.xlsx (b) The consolidated majority-vote annotations across five conceptual facets -> majority_vote_annotations.xlsx (c) The corpus enriched with OpenAlex citation statistics, both as a whole and as one file per facet -> papers_with_citation_stats_*.xlsx (d) The expert annotation ground truth and the per-model LLM annotations -> Human-results.xlsx, GPT4/Ollama/QWEN-results-cleanedAndUnified.xlsx (e) The analysis code: one Jupyter notebook for the citation analysis, and seventeen Python modules covering majority-vote consolidation, inter-annotator agreement, temporal trends and facet distributions -> citation_analysis.ipynb, *.py, dependency_map_code.pdf All tabular files are linked through the "Paper ID" key. Missing values are represented as empty cells. Where a facet label could not be assigned, the value "Other" is used in the Medical Category facet; the remaining facets have no residual category. Publication date: 2026-08-03Creation date: 2025-11-06 (OpenAlex citation retrieval date; see Section 3)Language: en