Welcome to the WISEST (WhIch Systematic Evidence Synthesis is besT) Project survey page. This survey is a component of a larger project to develop an AI decision support tool to assess the strengths and weaknesses of one or more systematic reviews when there are multiple on the same topic. Our target audience for the WISEST tool is decision makers and learners. The purpose of this survey was to understand how decision makers (e.g. students, clinicians, researchers or policymakers) evaluate and use systematic reviews in their decision making or learning. Specifically, when there are multiple systematic reviews on a particular question, do they pick one or more systematic reviews to use or read? Would they use a supporting tool with Artificial intelligence (AI) capability, if one was available, to help assess the strengths and weaknesses of the systematic reviews on their topic of interest? This project is led by a steering group of international experts in systematic review methodology including: Carole Lunny, Sera Whitelaw; Andrea Tricco, Ebrahim Bagheri, Ba’ Pham, Salmaan Kanji, Dawid Pieper, Bev Shea, Areti-Angeliki Veroniki, Clare Ardern, Karim Khan, Candyce Hamel, Emma Reid, Nicola Ferri, Yuan Chi, Janet Zhang, and the working group: Harrison Nelson, Lindy Pangka; Banveer Kalkat; Wendy Zheng; Reema Abdoulrezzak; Kevin Kang; Tasnim, Sara; Anmol Sooch; Sai Surabi Thirugnanasampanthar; Dian Wang; Parisa Safavi, and Cynthia Ramasubbu. This project has not received any targeted funding to date. Definitions: A systematic review attempts to collate all study-specific evidence that fits pre-specified eligibility criteria to answer a specific research question. It uses explicit, systematic methods that are selected with a view to minimising bias, thus providing more reliable findings from which conclusions can be drawn and decisions made. Traditional meta-analysis is a statistical method to combine the results from two or more primary studies (e.g. randomised controlled trials, cohort studies). comparing an intervention to a placebo/control or another intervention. Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Examples are Siri, Alexa, self-driving cars, and in the evidence synthesis realm RobotReviewer (https://www.robotreviewer.net). If you have any questions or comments, please contact Dr Carole Lunny at carole.lunny@ubc.ca -- Carole Lunny, MPH, PhD Postdoctoral Fellow, Methodology and Research Synthesis carole.lunny@ubc.ca @carole_lunny