Building Confidence in AI: A Practical Guide for Evidence Reviewers

Review teams are adopting AI at a fast pace. Tools that sort citations, extract data from full texts, and identify relevant studies are becoming standard in systematic and targeted literature reviews.

The time savings are real, but using AI in evidence review also raises important questions: How can you tell whether an AI output is reliable? Where can AI genuinely support your workflow, and where is human judgment still essential? And how can you clearly document and explain the role AI played in your review?

Whether you work in SLRs, TLRs, HEOR, Pharma, or Academia, this webinar will help you better understand how to use AI in evidence review. The aim is simple: help you gain the speed AI offers while keeping your reviews rigorous, traceable, and easy to defend.

Using EasySLR as a live reference, we will explore how AI and reviewers can work together across different stages of a review, from de-duplication and screening to data extraction. You will see how AI decisions can be checked against reviewer decisions, what the numbers behind "high accuracy" claims actually mean, and how to build a workflow where people stay in charge of the calls that carry the most weight.

We will also discuss where AI may not be the right choice. Not every task benefits equally from automation, and understanding those boundaries is an important part of responsible AI use in evidence review.

A good part of the discussion will focus on due diligence. Before any AI tool touches your project, you should know how its performance was measured, on what kind of literature, whether you can trace each output back to the source document, and what details your methods section will need.

Join us for a practical discussion on using AI in evidence review, with real workflow examples, practical considerations, and time for your questions.