I m building EvidenceFlow, an evidence-synthesis workspace for systematic reviews, and I would really value honest feedback from researchers, students, librarians, clinicians, epidemiologists, and anyone who has worked on a literature or systematic review.
Many teams use several tools across one review: a reference manager, screening software, spreadsheets for extraction, separate analysis software, and manual PRISMA reporting.
Which stage causes the most friction for you?
Designing the protocol, PICO, and eligibility criteria
I m interested in how researchers think about AI-assisted literature screening and data extraction.
AI can potentially help prioritize records, surface patterns, and reduce repetitive work. But systematic reviews require transparency, reproducibility, and human judgment especially for include/exclude decisions and extracted study data.
Where would you find AI assistance genuinely useful?
EvidenceFlow is a free-to-use evidence synthesis workspace for systematic reviews and meta-analysis. Build PICO questions, run blinded multi-reviewer screening, resolve conflicts, extract data, generate PRISMA 2020 flow diagrams, and create forest and funnel plots in one project. Core review workflows are free; AI-assisted screening and extraction are available when teams need to move faster.
