Artificial Intelligence in Healthcare, Volume 1: Foundations and Technical Principles

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 the unique challenges of 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 (Marcus, Yuki, Jamal, Elena, Priya, David, and Aisha) recur across the chapters to ground abstract methods in realistic patient scenarios, providing pedagogical continuity from the first chapter to the last. Inside this volume Machine learning fundamentals in clinical context Healthcare data quality, missingness, and preparation Evaluation metrics for clinical decision support, including calibration and fairness Convolutional neural networks for medical imaging Time series analysis for physiological monitoring and early warning Clinical natural language processing and large language models Fairness, bias, and algorithmic equity in clinical AI Model interpretability and explainability methods Privacy, security, and trustworthy deployment foundations Hands-on companion materials The textbook is accompanied by 47 Jupyter notebooks hosted on Google Colab, providing practical experience with real-world clinical AI workflows from data preprocessing through model evaluation. Notebooks are referenced from the text and can be completed in any order that suits the reader's learning path. Who this book is for Healthcare professionals seeking technical literacy in artificial intelligence; clinical informaticists building practical competencies; graduate students in health informatics, biomedical engineering, and AI in medicine; and researchers entering the healthcare AI domain. Adopted in graduate programs including the MSc in AI in Medicine at the University of Bern. About the AI in Healthcare series Volume 1: Foundations and Technical Principles teaches how AI works in clinical contexts. Volume 2: Implementation addresses how to validate and deploy AI safely. Volume 3: Scaling AI Across Healthcare covers specialized domains and emerging frontiers.