This first chapter explores the foundational principles, complexities, and challenges of medical diagnosis, highlighting its central role in clinical care. Accurate and timely diagnosis is essential for effective management, prevention, and improved patient outcomes. This chapter emphasizes the dynamic and iterative nature of the diagnostic process, which involves integrating diverse clinical data, hypothesis testing, and evidence-based reasoning. The chapter begins by defining medical diagnosis and discussing its importance in individual patient care and public health. The discussion turns to the traditional cognitive science underpinning medical diagnosis, emphasizing the dual processes of heuristic (System 1) and analytic (System 2) reasoning. Bayesian theory is introduced as a critical framework for refining diagnostic probabilities based on evolving clinical information. Cognitive biases that challenge diagnostic accuracy, such as anchoring, availability, and confirmation biases, are examined and paralleled with data biases in artificial intelligence, highlighting shared challenges in human and machine-assisted diagnostics. Key diagnostic performance metrics, involving sensitivity, specificity, and likelihood ratio, are detailed. The trade-offs inherent in these metrics are discussed in the context of clinical decision-making. Modern diagnostic complexities are addressed, alongside the risks of diagnostic errors, overdiagnosis, and underdiagnosis. Strategies to achieve diagnostic excellence are outlined, emphasizing a multidisciplinary, team-based approach, clinical decision support systems, and system-level improvements. The chapter concludes organizations’ initiatives to promote diagnostic excellence globally.