Control and data mining in healthcare applications are essential activities as they support decision-making processes. In this context, control and data mining imply a large number of medical and technical aspects, and the nonlinearities of the systems involved make them challenging. Intelligent paradigms in control and data mining are important to meet the high performance requirements of healthcare systems. The intelligent systems in healthcare applications described in this chapter involve three types of artificial intelligence techniques: Reinforcement Learning (RL) in control and modeling, also combined with neural networks cooperating with RL and classification algorithms, and metaheuristic optimization algorithms. The current chapter continues the chapter in the first edition of this handbook, keeping the first and last authors and adding new authors to update the content so as to capture the research results obtained in the meantime. This chapter presents some research results related to applications developed by the Process Control Group of Politehnica University Timisoara, Romania, highlighting that the intelligent paradigms, even if they are artificial, are able to model, control and make decisions in the context of various medical systems, being useful tools for prevention, early detection and personalized healthcare.