AI in review(s) 

Artificial intelligence (AI) is rapidly transforming the research landscape, including the way (systematic and scoping) reviews are conducted. From question formulation and search strategy development to screening and data extraction, a growing number of AI-powered tools promise to increase efficiency and reduce workload. Is this the reality? If so, how can we safely include AI in the systematic literature review cycle?

Content

This webinar provides an overview of AI applications across the entire systematic review workflow (from research question, to building a search strategy to data extraction). We will cover the opportunities, limitations, and methodological considerations associated with AI-assisted evidence synthesis. Particular attention will be given to transparency, reproducibility, critical thinking, and responsible use of AI tools.

Goals

After this webinar, participants will be able to better understand how AI can/cannot support different stages of the systematic review workflow, critically evaluate its limitations, and apply principles of responsible, transparent, and reproducible AI use in evidence synthesis.

Prerequisites

Please note that this webinar is not an introduction to systematic review methodology or prompting. Participants are expected to be familiar with the material covered in the following introductory modules:  

General steps to synthesising a review  

Responsible use of generative AI  

AI prompt engineering  

Registrants must also fill in a questionnaire before the course, which you will receive in your confirmation email.

Target Group: Students, PhD, UM staff, MUMC+ employees, researchers that work with evidence synthesis.

Language: English

Format: Webinar

 Date: July 3, 13:00 – 14:30 (1.5 hours)