Literature reviews are a critical component of evidence-based medicine, serving as a structured approach to addressing clinical questions by systematically analyzing the breadth of published academic literature. However, traditional methods require significant time, effort, and specialized expertise. This presentation introduces an advanced tool designed to automate key aspects of the literature review process. The tool offers: Keyword-based search across public biomedical databases. Advanced prompt engineering to refine criteria for paper inclusion and exclusion. Fact extraction tailored to extract and highlight essential data points from the target studies. Traceability and explainability features to ensure transparency and accountability in the results. A guided user interface that supports iterative refinement and validation, enabling users to fine-tune their reviews efficiently. This session explores the extent to which systematic reviews can be semi-automated using cutting-edge, healthcare-specific Generative AI models, and discusses the implications for the future of evidence-based medicine.