Built for researchers, by researchers
A rigorous systematic review routinely consumes months: thousands of abstracts screened in duplicate, data extracted by hand, statistics assembled across tools, and a reporting checklist reconstructed at the end. These tasks involve substantial repetitive work.
AutoEvidence exists to take the volume and leave the judgment. It automates search, deduplication, screening, extraction, and meta-analysis in one place, so you can manage evidence synthesis in one workflow. It supports your expertise; it does not replace it.
AI for volume, humans for judgment
Screening thousands of abstracts, extracting structured data, and computing pooled effects are volume problems that AI can help with. Deciding what a borderline abstract really means, whether studies are similar enough to pool, and what the evidence supports is scientific judgment. That stays with you. Every AI decision in AutoEvidence is inspectable and overridable.
Transparent by default
A systematic review is only as credible as its audit trail. AutoEvidence keeps one automatically: what was searched and when, why each record was excluded, how screening decisions were made, and the data behind the report. These records support PRISMA 2020 reporting.
Methods first
The workflow follows the stages of a systematic review: PICO-based protocols, multi-database searches, deduplication, dual screening with conflict resolution, risk-of-bias assessment, and synthesis.
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Questions, feedback, or a review you are planning? We read everything.