Before using this in research
The goal is not to adopt another tool. The goal is to reduce verified research time without weakening the evidence trail.
Best for
Medical students, clinical researchers, residents, PIs, and review authors who need to find relevant papers without losing search reproducibility.
First step
Write the question in PICO or PECO form, list synonyms and MeSH directions, then decide which tool is responsible for discovery, verification, citation mapping, and storage.
A safer workflow
- 1Create a baseline query in PubMed, Embase, Web of Science, Scopus, or another trusted bibliographic database.
- 2Use Elicit, Consensus, Semantic Scholar, or Suppr to expand terms, identify seed papers, and surface candidate studies.
- 3Use Scite, ResearchRabbit, Connected Papers, or Litmaps to inspect citation context, related-paper networks, and missing clusters.
- 4Move important papers into Zotero or another reference manager, then record databases, dates, query strings, inclusion logic, and full-text review decisions.
Watch-outs
- AI literature tools can miss important papers, over-rank convenient summaries, and fail to provide a reproducible search strategy.
- A novelty check, grant background scan, or group-meeting search is not the same as a formal systematic review search.
- Google Scholar and citation networks are useful for discovery, but final claims for manuscripts or grants still need database search records and source verification.
Evidence checks
- Can every important claim be traced to PMID, DOI, or a journal page?
- Did you keep the exact search string and search date?
- Did you compare AI-discovered papers against at least one structured database query?
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This English version is a curated decision page. The full current detail page remains available while the English library is being expanded.
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