The complete graduate-level introduction to artificial intelligence in clinical medicine.

This first volume of the AI in Healthcare series provides the technical foundations required for meaningful engagement with clinical artificial intelligence. Designed as an integrated learning experience for healthcare professionals, clinical informaticists, graduate students, and researchers, the text combines rigorous theoretical exposition with extensive practical application through 47 companion Jupyter notebooks that allow readers to implement concepts as they learn them.

Beginning with fundamental principles of machine learning and progressing through healthcare-specific considerations in data preparation, evaluation methodology, and model validation, the volume establishes the technical competencies required to evaluate, build, and reason about clinical AI systems. Specialized chapters address medical imaging analysis, physiological time series processing, and clinical natural language processing, including contemporary large language model applications. A dedicated final part covers the responsible AI essentials of fairness and bias, interpretability, and privacy and security.

Seven fictional clinical journeys recur across the chapters to ground abstract methods in realistic patient scenarios, providing pedagogical continuity from the first chapter to the last.