Moving beyond Excel for your literature matrix
Spreadsheets work for ten studies; they break at fifty. Version control, audit trails, and multi-reviewer conflicts are painful in Excel. A dedicated extraction workspace ties each cell to a source PDF and reviewer decision.
Searching "stop using Excel for meta-analyses" usually means you need structured, repeatable extraction, not another macro.
How to extract data from PDF to structured tables using AI
Define your extraction schema once (population, intervention, effect sizes, risk of bias fields). Upload or link PDFs for included studies; AI proposes values per field with citations to the source paragraph.
You correct errors before export. For teams that still need Excel, export CSV. The authoritative copy lives in the review workspace with full audit history.
- Schema generation from protocol outcomes.
- Batch extraction runs across included studies.
- Export for RevMan, R, or Stata prep.
Chatting with PDFs vs. structured extraction
"Chat with PDF" tools help you explore one paper quickly. They do not replace a literature matrix where every included study has the same columns for meta-analysis.
Use chat for clarification during full-text review; use schema-driven extraction when you need consistent columns across all studies for pooling.
Structure extraction from your protocol
Define columns once, extract from PDFs with AI drafts, and verify every field before analysis.
Common questions
Is AI extraction accurate enough for publication?
Treat AI output as a draft. Published workflows require human verification of every extracted field. Meta-analysis360 is designed for human-in-the-loop validation, not silent autopilot.
Can I export to Excel?
Yes. CSV/Excel export is supported for interoperability. The goal is to stop Excel from being your only source of truth during extraction.