Available Until 12/31/2024

Communicating with Researchers about the Benefits and Risks of AI for Evidence Synthesis (Recording)

It seems that everyone is talking about AI and what it can do, can’t do, and seems to do but doesn’t really. If you work on systematic reviews, you may be especially interested in the answers to these questions that go to the heart of how librarians collaborate with researchers on reviews:

  • Can AI tools automate and expedite systematic reviews and other types of evidence synthesis without compromising the rigor and reproducibility that are the hallmark of systematic reviews?
  • How do you critically appraise a study that uses AI?
  • How do you have informed conversations with and advise researchers on the use of AI in reviews in this time of uncertainty and rapid change?

Gregory Laynor, a systematic review expert who has been studying the impact of AI on evidence synthesis, will be your guide to having conversations with research teams about the capabilities of current AI tools and the risks they present for systematic review rigor and reproducibility. You’ll learn:

  • the questions to ask researchers who are interested in using AI for evidence synthesis
  • how to report the use of AI in an evidence synthesis project
  • what tools are available for critically appraising studies conducted with AI methods.

Through interactive scenarios, you will practice examining and communicating with research teams about the benefits and risks of AI.

You’ll leave the webinar with new knowledge and increased confidence in your ability to discuss AI with researchers, and you’ll join the company of librarians able to build on their expertise in the rigor and reproducibility in systematic reviews to help shape the future of AI.

This webinar is required for Level II of the Systematic Review Services Specialization.

Learning Outcomes

By the end of the webinar, you will be able to:

  • Identify types of AI that can support the systematic review process.
  • Address concerns about how AI may compromise the validity and reproducibility of reviews.
  • Identify and employ tools for critically appraising studies conducted with AI methods
  • Have informed conversations about AI with systematic review research teams when planning systematic review projects.


Health sciences librarians and other information professionals who work on evidence synthesis projects and have an interest in artificial intelligence. Prior knowledge of AI is not required.


Gregory LaynorGregory Laynor, MLS, PhD is Assistant Curator and Systematic Review Librarian in the NYU Health Sciences Library at NYU Grossman School of Medicine. He teaches a course on systematic review methods in the Department of Population Health at NYU and is also on the faculty of the Population Health doctoral program in the College of Population Health at Thomas Jefferson University. He writes a column on emerging technologies and their impact on healthcare, research, and education for the Journal of Electronic Resources in Medical Libraries and has published on the impact of AI on systematic reviews and evidence-based medicine.

Registration Information

  • Length: 1.5 hour recorded webinar
  • Technical information: After you have registered, go to My Learning in MEDLIB-ED to access the live webinar, resources, evaluation, and certificate.
  • Register, participate, and earn 1.5 MLA continuing education (CE) contact hours.

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