This chapter provides a systematic examination of artificial intelligence applications in medical science, outlining a comprehensive development pipeline from conception to clinical implementation. Beginning with foundational concepts, it explores the full spectrum of AI system creation—including data collection protocols, computational algorithm architectures, laboratory technical requirements, and human–computer interaction design—while emphasizing ethical considerations throughout the development process. Focus is given to feature engineering methodologies and their influence on predictive model performance, accompanied by an analytical survey of both established and emerging techniques in medical AI. The discussion encompasses conventional machine learning approaches as well as advanced ensemble methods. By synthesizing current technological capabilities with persistent implementation challenges, this chapter offers a primer on the fundamentals of medical AI. It also provides a forward-looking perspective on the field's transformative potential in healthcare.