CLINICAL MACHINE LEARNING

Clinical Machine Learning: From Data to Deployment is a rigorous, end-to-end introduction to building machine learning systems that are actually safe to use in the care of real patients. Written for numerate readers — biomedical engineers, data scientists, clinicians, and researchers — it treats validation as a science, not a checkbox, and is unafraid of the mathematics that separates a paper from a product.

The book develops the full lifecycle of clinical ML in six parts. Parts I and II build the mathematical and methodological foundations: the translational gap and why medical AI fails, the structure and built-in bias of medical data, probability and statistical inference for clinical reasoning, linear algebra and optimization, regularized regression, classification and decision thresholds, tree ensembles, and unsupervised phenotype discovery. Part III develops deep learning as it is actually used in medicine — neural networks from first principles, convolutional networks for medical imaging, sequence models for waveforms and patient trajectories, and transformers, foundation models, and clinical language. Part IV, the largest, develops the validation, uncertainty, calibration, clinical utility, external validation, dataset shift, and shortcut-learning apparatus that separates a research artifact from a deployable system. Parts V and VI move to deployment, regulation, fairness, and research practice.

A three-track reading path — Foundation, Implementation, and Frontier — lets students, practitioners, and researchers each read the book at their own depth. Throughout, the tone is honest about what works and what does not, and firm about the ethical guardrails (including a hard rule against sending credentialed patient data to third-party APIs) that trustworthy medical AI demands.

Whether you are a data scientist, clinician, engineer, or researcher, this book equips you with the knowledge and tools to turn data into reliable, ethical, and impactful clinical AI.