This paper examines the transformative potential of Artificial Intelligence (AI) in enhancing evidence-based practice (EBP) within healthcare. By leveraging AI-driven clinical decision support systems, natural language processing, and advanced diagnostic tools, the study explores how these technologies can streamline the synthesis and application of medical evidence to improve clinical decision-making and patient outcomes. Through a comprehensive literature review and analysis of case studies, we highlight the significant impact of AI on reducing administrative burdens, minimizing diagnostic errors, and enabling personalized care. In addition to these benefits, the paper also addresses key challenges such as ethical concerns, technical limitations, and potential biases. The findings underscore the need for continued interdisciplinary collaboration and the development of transparent and adaptive AI systems to ensure that these innovations effectively complement and enhance clinical workflows.
Keywords: Artificial Intelligence, Clinical Decision Support, Evidence-Based Practice, Healthcare, clinical data analysis, deep learning, natural language processing, reinforcement learning
