AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI
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 identify key failure modes. Our results demonstrate that agentic AI can substantially accelerate scientific evidence synthesis in specialised domains.
69c3356995b48f16a30076eb
Shreyansh Padarha
claimed_verified
2026-03-26T14:58:24.444Z
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Shreyansh Padarha
shreyanshpadarha
user
shreyanshpadarha
69c3356995b48f16a30076ec
Ryan Othniel Kearns
claimed_verified
2026-03-26T14:04:18.794Z
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Ryan Othniel Kearns
ryanothk
user
ryanothk
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Tristan Naidoo
69c3356995b48f16a30076ee
Lingyi Yang
69c3356995b48f16a30076ef
Łukasz Borchmann
claimed_verified
2026-03-25T10:50:57.505Z
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Łukasz Borchmann
Borchmann
user
Borchmann
69c3356995b48f16a30076f0
Piotr BŁaszczyk
69c3356995b48f16a30076f1
Christian Morgenstern
69c3356995b48f16a30076f2
Ruth McCabe
69c3356995b48f16a30076f3
Sangeeta Bhatia
69c3356995b48f16a30076f4
Philip H. Torr
69c3356995b48f16a30076f5
Jakob Foerster
69c3356995b48f16a30076f6
Scott A. Hale
69c3356995b48f16a30076f7
Thomas Rawson
69c3356995b48f16a30076f8
Anne Cori
69c3356995b48f16a30076f9
Elizaveta Semenova
69c3356995b48f16a30076fa
Adam Mahdi
AgentSLR: Automating Systematic Literature Reviews in Epidemiology with Agentic AI
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 identify key failure modes. Our results demonstrate that agentic AI can substantially accelerate scientific evidence synthesis in specialised domains.
Large language models can automate systematic literature reviews with human-level performance while reducing review time from weeks to hours.
systematic literature reviews large language models agent-based systems evidence-based policy automated review workflows article retrieval article screening data extraction report synthesis expert-curated ground truth agentic pipeline human-in-the-loop validation