Full-Cycle Automation of Scientific Literature Review Processes

Why this matters

While significant progress has been made automating individual steps such as screening or extraction, fewer than 2% of studies have explored end-to-end automation of the complete literature review cycle. Integrating these components into coherent, reliable pipelines represents a qualitatively different and higher-order challenge that the field must address to deliver transformative research efficiency gains.

Suggested approaches

• Design modular pipeline architectures that chain screening, extraction, synthesis, and quality assessment components, with systematic evaluation of error propagation across stages
• Conduct human-in-the-loop studies that identify which stages of the review cycle most benefit from automation versus human oversight, optimizing hybrid workflows
• Develop end-to-end benchmarks that evaluate full pipeline outputs against gold-standard systematic reviews produced by expert teams

Expected impact

Achieving reliable full-cycle automation would reduce the time and cost of systematic reviews by orders of magnitude, democratizing evidence synthesis for under-resourced research communities and enabling rapid evidence updates in fast-moving fields.

A question to explore

I want to investigate the feasibility and failure modes of full-cycle AI automation of literature reviews. What does the evidence tell us about integration challenges across pipeline stages, and what would a rigorous study design look like to evaluate end-to-end automated review systems against expert human benchmarks?