I m Robin, building EvidenceFlow: a workspace intended to connect systematic-review planning, multi-reviewer screening, data extraction, PRISMA reporting, and meta-analysis.
The problem I am trying to solve is not that researchers lack tools. It is that a typical review often involves several tools and spreadsheets, with manual handoffs between them.
I would value direct feedback from anyone who has built or used complex workflow software:
Which step should remain specialized rather than being pulled into an all-in-one platform?
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?
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
