The rapid growth of electronic health records and biomedical repositories has created unprecedented opportunities to extract patterns from medical data. Among the most effective approaches for uncovering hidden relationships is association rule mining, a data mining technique designed to identify patterns and correlations among variables. In recent years, advances in artificial intelligence (AI) have significantly enhanced interpretability when dealing with complex medical datasets. AI Techniques for Association Rule Mining in Medical Data: Trends and Practical Applications explores how AI is revolutionizing the process of discovering patterns and relationships in medical datasets. It examines how hybrid and intelligent models enable more accurate and clinically relevant pattern discovery in high-dimensional medical datasets. Covering topics such as AI, medical data, and medical technologies, this book is an excellent resource for academicians, data scientists, healthcare professionals, software engineers, AI developers, and graduate students.