Faster Systematic Reviews with AI

Software Service (Coming Soon)

Accelerate your systematic reviews with transparent, AI-assisted workflows.
This software service helps researchers screen literature more efficiently—without giving up control. It supports a wide range of research frameworks (like PICO, SPIDER, and SPICE) and helps you screen studies faster using AI. You train the system by labeling a sample of papers, and it learns to suggest decisions for the rest—always under your control. Every step is logged for full transparency.
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Now accepting pilot testers

We’re currently preparing for the public launch and are looking for researchers who want early access to test the software service and help shape its development. If you're planning a systematic review—or have completed one that could be used as a benchmark—and are open to experimenting with AI-supported workflows, we’d love to hear from you.
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👉 Fill out our contact form‍

Features

✅ Support for multiple research frameworks
Define your inclusion criteria using the structure that fits your review. Create a custom research framework or use one of the suppported widely used frameworks, including:

• PICO – Population, Intervention, Comparison, Outcome: standard in evidence-based medicine.
• PICOT – adds Time for duration/follow-up in clinical trials.
• PICOS – adds Study design (e.g., RCTs, cohort studies).
• SPIDER – for qualitative/mixed-methods (Sample, Phenomenon, Design, Evaluation, Research type).
• SPICE – for policy/service evaluations (Setting, Perspective, Intervention, Comparison, Evaluation).
• PEO – for observational/qualitative studies (Population, Exposure, Outcome).
• PICo – for nursing/health contexts (Population, Phenomenon of Interest, Context).
• ECLIPSE – for health policy/service improvement (Expectation, Client, Location, Impact, Professionals, Service).

✅ AI-assisted title and abstract screening
Label an initial batch of studies manually. The AI learns from your decisions and suggests labels for the rest—tailored to your inclusion criteria.

✅ Human-in-the-loop workflow
You’re always in control. Accept, reject, or correct AI-suggested labels based on your own judgment.

✅ Automatic bulk labeling (when ready)
The interface shows when the AI is still learning or ready to label the remaining papers with high confidence. You stay in control and can double-check or override any of its decisions.

✅ Full auditability
Every decision—both human and AI—is logged for complete transparency and reproducibility.

✅ Parallel screening with multiple reviewers (coming soon)
Easily invite multiple reviewers to independently review the same study set using your defined selection criteria—ideal for parallel screening and inter-rater reliability.
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Collaboration and Funding

The AI-supported systematic review software service was developed in collaboration with:

• Erwin Haas and Judith van den Bosch (Selectical)

For more information about Selectical, visit their website. The development of this software service was financially supported by an R&D collaboration grant from the Province of South Holland, the Netherlands.