This chapter demonstrates how intelligent systems are applied to real-world biomedical and healthcare challenges. It begins with AI in medical imaging and diagnostics, describing imaging modalities, preprocessing, deep learning models, clinical validation, and system integration (Sect. 2.1). Intelligent methods for evaluating and maintaining medical device performance are then presented, highlighting predictive maintenance and regulatory compliance (Sect. 2.2). Diagnostic support systems and clinical decision-making frameworks follow, showing how machine learning enhances rare disease diagnosis and real-time clinical guidance (Sect. 2.3). AI-driven personalized medicine is explored through patient stratification, genomics and transcriptomics integration, drug repurposing, virtual screening, predictive treatment modeling, and digital twin development (Sect. 2.4). Wearable biosensors and remote health monitoring are discussed next, focusing on sensor types, signal acquisition, anomaly detection, and edge analytics for chronic disease management (Sect. 2.5). Robotics and AI in surgery and rehabilitation are then examined, covering robot-assisted surgery, surgical planning, exoskeletons, brain–computer interfaces, and rehabilitation robotics (Sect. 2.6). The chapter concludes with ethical, legal, and regulatory considerations, addressing AI training practices, data privacy, bias mitigation, transparency, and compliance with international standards such as FDA and EMA guidelines (Sect. 2.7).