This thesis addresses the challenges healthcare professionals face in implementing Evidence-Based Medicine (EBM) in clinical practice. Despite recognizing EBM's importance, clinicians struggle with time constraints, limited methodological knowledge, and language barriers when evaluating scientific literature. The research presents an integrated artificial intelligence solution addressing each stage of the EBM process. The system includes a Risk of Bias assessment tool that embeds research papers in a vectorial database and employs Retrieval-Augmented Generation (RAG) with Large Language Models to evaluate study quality. This component achieved a weighted mean accuracy comparable to GPT models' performance. For medical data extraction, the thesis developed a RAG-enhanced language model that outperformed GPT models across multiple study designs.