systematic-literature-review

To automate a systematic literature review, use an AI-assisted workflow to plan multi-source searches, deduplicate results, score papers, and output ready-to-use LaTeX and BibTeX artifacts with provenance and validation checks.

AI-driven scoring applies unified algorithms to evaluate discovered papers, helping researchers select high-quality references and generate structured reports with reproducible word budgets and QA checks.

Deduplicating BibTeX entries requires a workflow that ingests multi-source search results, removes redundant records, and outputs clean BibTeX artifacts with provenance tracking and QA validation checks.

Yes, the literature review workflow outputs ready-to-use LaTeX and BibTeX artifacts, ensuring selected references and structured reports include provenance and QA validation checks for reproducible research.

Yes, you can use OpenAlex to plan and execute multi-source literature searches, followed by deduplication, AI-driven scoring, and topic modeling to select high-quality references for a structured report.

The best way to ensure reproducibility in automated literature reviews is to use a unified AI-driven workflow with validation steps, provenance tracking, and reproducible word budgets when drafting the final structured report.