Literature AI is only useful if the citations are real and the coverage is broad. Here is the checklist researchers should apply, and how CATO answers it.
Search breadth
One query in CATO fans out across PubMed, ClinicalTrials.gov, bioRxiv and medRxiv, arXiv, OpenAlex, Semantic Scholar, and patent databases, with deduplication and ranking. Single-database tools miss preprints and trials.
Evidence extraction
CATO extracts PICO elements (population, intervention, comparison, outcome), study design, and sample size from retrieved works, so screening is structured rather than ad hoc.
Citations you can verify
Every claim in a generated document carries an inline citation to a real record, and a QA step flags anything uncited. Fabricated references, the classic chatbot failure, are designed out.
From reading to analysis
Findings flow into the same workspace where the data analysis happens, and finished reviews export to Word. CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.