[2603.22327] AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI

Computer Science > Information Retrieval arXiv:2603.22327 (cs) [Submitted on 20 Mar 2026] Title:AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI Authors:Shreyansh Padarha, Ryan Othniel Kearns, Tristan Naidoo, Lingyi Yang, Łukasz Borchmann, Piotr BŁaszczyk, Christian Morgenstern, Ruth McCabe, Sangeeta Bhatia, Philip H. Torr, Jakob Foerster, Scott A. Hale, Thomas Rawson, Anne Cori, Elizaveta Semenova, Adam Mahdi View a PDF of the paper titled AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI, by Shreyansh Padarha and 15 other authors View PDF Abstract:Systematic literature reviews are essential for synthesizing scientific evidence but are costly, difficult to scale and time-intensive, creating bottlenecks for evidence-based policy. We study whether large language models can automate the complete systematic review workflow, from article retrieval, article screening, data extraction to report synthesis. Applied to epidemiological reviews of nine WHO-designated priority pathogens and validated against expert-curated ground truth, our open-source agentic pipeline (AgentSLR) achieves performance comparable to human researchers while reducing review time from approximately 7 weeks to 20 hours (a 58x speed-up). Our comparison of five frontier models reveals that performance on SLR is driven less by model size or inference cost than by each model's distinctive capabilities. Through human-in-the-loop validation, we id...