Conducting systematic reviews can be a daunting task, especially when faced with thousands of citations. Abstrackr, developed by Brown University's Center for Evidence Synthesis in Health, offers a free, open-source solution to simplify this process. Designed to semi-automate the citation screening phase, Abstrackr leverages machine learning to predict the relevance of studies based on titles and abstracts, significantly reducing the manual workload. Key Features of Abstrackr AI-Powered Recommendations: Utilize machine learning algorithms to identify and prioritize relevant citations, allowing researchers to focus on the most pertinent studies. Collaborative Screening: Invite team members to participate in the screening process, facilitating efficient…