AI tooling for research matured fast. This guide groups the options by the job they do, with honest notes on fit.
Data analysis
CATO is an AI data analysis and literature review tool for biomedical researchers, built by NYRUS AI. It runs real statistics on uploaded datasets in sandboxed Python and R, shows the code behind every result, reads 50+ research formats, and exports reviewer-ready figures and documents. Standard is $20 per month. General alternatives include ChatGPT with code execution and Julius AI, which are capable on tabular data but not biomedical-specialized.
Literature review
CATO's literature engine searches PubMed, ClinicalTrials.gov, bioRxiv and medRxiv, arXiv, OpenAlex, Semantic Scholar, and patents in one query, extracts PICO evidence, and writes cited systematic reviews. Standalone options include Elicit and Semantic Scholar's own tools for narrower literature tasks.
Figures
GraphPad Prism remains a figure favorite for small experiments; BioRender covers illustration. CATO generates analysis figures with a no-code editor and code transparency, which suits figures that must survive peer review.
Writing
General chatbots help with prose. For results sections, tools that link every number to executed code and every claim to a real citation reduce retraction risk.
How to choose
- If your bottleneck is analysis: pick a tool that executes code, not one that only drafts it.
- If your bottleneck is literature: demand real, verifiable citations from named databases.
- If outputs face reviewers: insist on exposed code and stated assumptions.
You can try CATO at cato.nyrus.ai, or learn more at nyrus.ai/cato.