Best: ChatGPT or Claude for literature reviews?

Short answer / recommendation
Use Claude as your primary model for systematic reviews and meta-analyses (long-context extraction, structured synthesis). Keep ChatGPT as a secondary tool for quick drafting, formatting, and plugin-based retrieval when needed.

Why (one-paragraph rationale)
Systematic reviews need large-context reading, consistent extraction tables, and careful analytical prompts. Claude is designed for long-context, careful analysis and tends to handle multi-document syntheses and complex instruction-following better in one pass. ChatGPT is still useful for iterative wording, citation formatting, and for workflows that rely on its plugin ecosystem (reference managers, web retrieval).

Decision criteria (pick which matter most for your project)
- Context window / document length: prefer Claude for hundreds of pages or many PDFs in one session.
- Provenance / citations: both can hallucinate—choose workflow that forces provenance capture and human verification.
- Integration: choose ChatGPT if you need specific plugins (Zotero, web scraping) out of the box.
- Cost & access: free tiers are limited; larger context/APIs cost more—budget accordingly.
- Reproducibility: prefer the model and tooling that lets you log prompts, model versions, and outputs.

Best-for / Avoid-if
- Claude: Best for long multi-document synthesis, extracting structured fields (PICO, methods, effect sizes) and keeping context. Avoid if you require specific live web lookups or an ecosystem of plugins you already use.
- ChatGPT: Best for quick edits, drafting PRISMA sections, or when you rely on third-party integrations. Avoid for one-shot synthesis of many long documents unless you chunk carefully.

Practical checklist to run a reliable, reproducible AI-assisted review
1) Prep: Gather PDFs, export citations to a reference manager (Zotero/EndNote) and deduplicate. Keep a master spreadsheet for provenance (DOI, page range, filename).
2) Chunking & ingestion: Split long PDFs into 2–5 page chunks, label each chunk with source + page numbers. Feed chunks to the model in order to preserve context.
3) Prompt templates (use fixed templates and log them): extraction template should request structured outputs (study ID, population, PICO, sample size, outcomes, effect size, CI, methods, risk-of-bias rating, direct quote with page number, and confidence score). Example: “Extract into CSV columns: StudyID, DOI, Page, Outcome, EffectSize, CI, N, RiskBias, Quote (include exact text + page).”
4) Ask for provenance per item: require exact quoted sentences + page numbers and the original filename/DOI. Don’t rely on the model’s citation strings alone—treat them as pointers to verify.
5) Human verification: Audit 100% of extracted effect sizes and study IDs against the original PDF. Flag any extraction where the model’s confidence is low or where numbers don’t match.
6) Reproducibility: Save prompts, model name/version, timestamps, and raw model outputs. Store final extraction tables in CSV and link to PDFs.
7) Meta-analysis step: Use a stats package (R/metafor or RevMan). Export model-provided numeric tables and verify everything before running pooled analyses.

Final tips
- Always verify numerical data and quotes against source PDFs. AI accelerates extraction but does not replace manual checking.
- If budget/skill is limited: use ChatGPT for small reviews or screening + Claude for synthesis once you have extracted, verified data.
- Log everything (prompts, model version, edits) so your review can meet PRISMA and reproducibility expectations.

If you want, I can provide: a ready-to-use extraction prompt template and a chunking script suggestion.

Compare Claude and ChatGPT