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Whitepaper

Building trust in AI-assisted SLR: A reinvention of systematic literature reviews

Discover what it takes to make AI-assisted systematic literature reviews defensible. This white paper gives you the questions and evidence to evaluate whether an AI tool deserves your trust.

Do you trust AI in Systematic Literature Review workflows?

You’re expected to move faster on evidence-based decisions, but the literature keeps growing faster than your team can screen by hand. Miss one relevant study, and a trial’s direction—or a regulator’s trust in your submission—can shift.

Identifying evidence manually across
abstracts and full text is time-intensive.
With SLR demand rising and the body of
accessible literature expanding, teams
must do more with less, making AI an
essential part of the workflow.
Tim Cheney Technical Solutions Director, Digital Science

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The full whitepaper on building AI-assisted SLR trust, straight to your inbox.

This whitepaper is for you if…

You run or oversee systematic literature reviews in a regulated life sciences environment.

  • You need every AI-assisted answer traceable to its source, not just plausible-sounding
  • You’re evaluating AI tools for SLR work but remain cautious about hallucinated references
  • You want faster screening without sacrificing audit-ready rigor
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What you’ll discover

This whitepaper gives teams a framework for evaluating whether AI-assisted review can be trusted, and where the gaps are likely hiding.

  • The questions to ask any AI vendor before you trust its answers
  • Why traceability, not technical literacy, decides whether a review holds up to audit
  • How one medtech team cut SLR screening time by 63% without sacrificing rigor
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