Systematic review is a process used widely in evidence-based toxicology and environmental health research to identify, assess, and integrate the primary scientific literature with the goal of answering a specific, targeted question in pursuit of the current scientific consensus. Sciome received SBIR funding to conduct research and development to enhance our web-based, collaborative systematic review software application, SWIFT-Active Screener. By employing “Active Learning” machine learning methodology, and through a novel statistical method that can accurately estimate the percentage of relevant studies screened, Active Screener can significantly reduce the overall screening burden compared to traditional approaches. Dr. Ruchir Shah of Sciome presented a session on SWIFT-Active Screener during The Toxicology Forum Workshop in Belgium. His presentation highlighted rigorous testing of SWIFT-Active Screener on 26 different systematic review datasets, demonstrating robust performance of prioritization and recall estimation methods in a variety of real-world scenarios. For reviews with 5,000+ documents, Sciome reports an average reduction in screening burden of 61% (to obtain 95% recall). Active Screener has been used successfully to reduce the effort required to screen articles for systematic reviews conducted at a variety of organizations including NIEHS, EPA, USDA, TEDX, and EBTC.