Elicit Science screenshot

What is Elicit Science?

Elicit is an AI research assistant designed to help researchers conduct systematic literature reviews more efficiently. It uses machine learning to read and analyse academic papers, extract relevant data, and identify patterns across large bodies of research. Rather than manually scanning hundreds of papers, you can upload documents or search academic databases directly through the tool, and Elicit will help you categorise findings, spot contradictions, and synthesise results. The tool is particularly useful for academics, PhD students, and research professionals who need to review extensive literature as part of their work. It handles the tedious initial screening phase of literature reviews, which typically involves reading abstracts and deciding which papers are relevant to your research question. By automating this process, researchers can focus their time on deeper analysis and critical evaluation of the most relevant studies.

Key Features

Paper screening

automatically extracts information from academic papers and summarises key findings

Database search integration

searches across academic databases and retrieves relevant papers based on your research question

Data extraction

identifies and extracts specific data points, methodologies, and results from papers

Synthesis and analysis

helps identify patterns, contradictions, and gaps across multiple papers

Custom tagging and organisation

categorise papers and findings according to your review framework

Export functionality

save results in formats suitable for further analysis or publication

Pros & Cons

Advantages

  • Significantly reduces time spent on initial paper screening and data extraction
  • Helps identify relevant papers you might otherwise miss in large literature sets
  • Provides consistent extraction of key information across dozens or hundreds of papers
  • Accessible to researchers without advanced technical skills
  • Free tier allows you to test the tool before committing to paid features

Limitations

  • AI-generated summaries and extractions should be verified by human reviewers; automation is not perfect and can miss context or introduce errors
  • Limited to the accuracy of underlying academic databases and the clarity of paper content
  • May require you to refine search queries and review parameters multiple times to get best results

Use Cases

Conducting a systematic review for a PhD thesis or research project

Screening papers for a meta-analysis across multiple studies

Building a thorough literature map for a new research area

Tracking how a specific topic or methodology has evolved over time across published research

Identifying contradictory findings or gaps in existing research literature