Why evaluating AI-enabled literature review software takes more than a demo?

Systematic literature reviews (SLRs) and targeted literature reviews (TLRs) are essential to evidence generation across pharmaceuticals and the healthcare research sector. However, searching, screening, data extraction, and reporting still remains time and resource intensive. 

AI-assisted SLR software promises to streamline these workflows. But how can teams determine whether a platform delivers meaningful improvements in practice?

A demo showcases features, and a free trial provides access, yet neither shows how a platform actually performs against your research requirements and quality standards.

"The real test is whether AI improves efficiency while maintaining accuracy, transparency, and human oversight."

The problem with self-guided trials.

Without a structured evaluation, teams risk exploring features without reaching a clear conclusion.

Common challenges include:

• Unclear Objectives: No predefined performance metrics for measuring success.

• Unrepresentative Testing: Generic examples that don't reflect real research requirements.

• Limited Quality Assessment: Faster screening or extraction without measuring accuracy and correction effort.

• Disconnected Workflows: Testing individual features without assessing the complete review process.

• No Clear Decision: Finishing a trial without sufficient evidence to justify adoption.

• Unclear Team Responsibilities: Team members struggle to coordinate tasks and responsibilities on the platform.

For teams conducting SLRs, HTAs, and evidence synthesis, the focus should be on measurable outcomes, not simply the number of AI features available.

What should you evaluate?

A practical evaluation of AI-enabled evidence synthesis software should focus on four areas.

1. Screening Efficiency and Quality

Assess Title-Abstract screening, Full-Text screening, and eligibility decisions against predefined criteria. Measure processing time, reviewer effort, and how frequently AI recommendations are accepted or corrected. Where possible, compare results against a manually screened reference set.

2. Data Extraction Accuracy

Test structured data extraction using real publications and your team's extraction form. Validate outputs against source documents and assess the effort required to identify and correct errors.

3. Transparency and Reproducibility

Evaluate whether the platform provides traceable screening decisions, source-grounded extraction, review histories, and documentation to support quality control and reporting.

4. End-to-End Workflow Efficiency

Assess how effectively the platform connects searching, screening, extraction, and reporting, including collaboration and data export. The goal is to understand its impact on the overall review process.

A four-week guided evaluation framework.

A structured four-week evaluation helps teams assess AI-assisted literature review software against a real project and predefined success criteria.

Week 1: Setup and Alignment

Define evaluation goals, configure a workspace around a real review, onboard the team, and agree on measurable success criteria.

Outcome: A shared evaluation plan with clear objectives.

Week 2: Screening Workshop

Test AI-assisted screening and assess efficiency, decision quality, and reviewer effort. Gather feedback from the team using actual project records.

Outcome: Initial evidence of screening performance and workflow fit.

Week 3: Data Extraction and Evidence Generation

Run structured data extraction using your team's requirements. Validate outputs against source publications and assess consistency, quality checks, and reporting readiness.

Outcome: A clearer picture of extraction quality, traceability, and workflow efficiency.

Week 4: Outcome Review and Recommendation

Compare results against the agreed criteria. Review efficiency, quality, reviewer feedback, and implementation requirements to determine whether to proceed, extend testing, or stop.

Outcome: An evidence-based go/no-go decision.

From software access to an informed decision.

The value of evaluating AI-assisted SLR software lies in understanding how it performs under real working conditions.

A guided approach helps teams move beyond feature comparisons and determine whether AI-enabled workflows deliver meaningful improvements without compromising review quality or transparency.

The goal isn't simply to complete a trial. It's to make a decision supported by evidence.

You're never evaluating alone!

Evaluating new software shouldn't mean navigating unfamiliar features and workflows on your own.

With EasySLR's Guided Evaluation Program, our team works hand in hand with yours throughout the four weeks: setting up a real review project, testing screening and data extraction workflows, answering questions, and assessing results against your requirements.

By the end, you'll have hands-on experience, a clearer understanding of what value EasySLR can bring, and the confidence to make an informed decision.

Ready to get started? Let's explore a guided evaluation for your next systematic or targeted literature review.