How to Enhance AI Performance for Optimal Results?

How to Enhance AI Performance for Optimal Results?

EasySLR’s AI can streamline screening by providing inclusion/exclusion suggestions based on your protocol. However, to get the best results, it's important to first calibrate the AI to ensure it aligns with your decision-making. The steps below outline how to do this effectively.

Step 1: Upload a Sample Set for Evaluation
Start by uploading a sample of 100–200 citations to your project. This smaller, controlled dataset is ideal for testing how well the AI performs in comparison to human reviewers. We recommend using a representative sample that includes both clear includes and excludes for balanced evaluation.

Step 2: Use Protocol Optimiser for Targeted Suggestions
EasySLR’s Protocol Optimiser helps you strengthen your protocol to improve AI alignment:
  • It suggests specific edits to clarify ambiguous criteria.
  • Recommendations may include refining definitions, adding examples, or adjusting key inclusion/exclusion phrases.
  • Apply suggested changes.
Refer to the help article for a step by step guide on Protocol Text Review & AI Performance Analysis.

Step 3: Enable and Run AI Screening
Once your sample set is uploaded:
  • Enable Title & Abstract AI in the Settings. 
  • Allow the AI to generate inclusion/exclusion suggestions based on the protocol.
  • The AI will provide suggestions along with rationale.
Step 4: Manually Screen the Same Sample
Review and screen the same sample manually:
  • Include or exclude articles based on your judgment.
  • This creates a reference dataset for comparing the AI’s decisions.
Step 5: Compare AI and Human Decisions
Refer to the help article for a step by step guide on how to Compare AI and Human Reviewer Decisions.

Step 6: Recalibrate and Rerun AI
If more protocol changes are required, update and:
  • Rerun the AI on the sample to see if performance improves.
  • This calibration process may take a few iterations, but it’s critical to ensure accuracy in large-scale reviews.
Once you're confident in the AI’s decisions:
  • Upload the remaining citations.
  • Run the AI on the remaining citations and complete the screening.
Pro Tips:
  • Try to include a mix of relevant/irrelevant articles in the sample set.
  • Use the AI notes and rationale to understand how the AI is interpreting protocol.
Checklist before moving to full dataset:
  • Protocol finalized and saved
  • Sample set reviewed manually
  • Conflicts between AI and human resolved
  • Protocol Optimiser suggestions applied (if needed)
  • AI performance metrics (recall, conflict %) within acceptable range

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