[Scientists] AI Tools for Literature Review: Elicit, Consensus, and Beyond

Topic Overview

Comprehensive review of AI-powered literature review tools that can save researchers up to 80% of time on systematic reviews. Practical comparison and use cases.

Key Points to Cover

• Elicit: finding 1000 papers and analyzing 20000 data points
• Consensus for scientific evidence synthesis
• Semantic Scholar for citation analysis
• Best practices for combining AI tools with traditional review

Potential Sources

• Elicit: https://elicit.com/
• Sapio Sciences AI Tools Guide: https://www.sapiosciences.com/blog/10-scientific-ai-tools-every-scientist-should-know-in-2025-26/
• Berkeley Lab AI in Science: https://newscenter.lbl.gov/2025/09/04/how-berkeley-lab-is-using-ai-and-automation-to-speed-up-science-and-discovery/
• Frontiers Agentic AI in Labs: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1649155/full

Target Publication Date

TBD

Notes

Include real workflow examples and limitations of each tool. Address when human review is still essential.

Start by reviewing the listed sources and organizing the article around Elicit, Consensus, Semantic Scholar, and combined workflows. Include practical examples, limitations, and when human review remains essential; the work is done when the comparative literature-review article is complete and ready for a publication date.