Artificial intelligence is transforming modern healthcare by enabling powerful tools for disease detection, clinical decision support, and medical data analysis. This book presents advanced AI methods that extend beyond traditional machine learning models and demonstrates how these technologies can be applied in real medical environments. It explores privacy-preserving approaches such as federated learning that allow hospitals to collaboratively train predictive models without sharing sensitive patient data. The book also introduces explainable AI techniques that help clinicians understand how medical predictions are generated, improving trust and transparency in automated systems.
Readers will discover how modern language models can analyze clinical text, summarize medical reports, and assist with medical question answering. In addition, the book examines generative AI methods capable of producing realistic synthetic medical data to support research and model development while protecting patient privacy. Practical examples, real datasets, and hands-on tutorials guide readers through the implementation of state-of-the-art techniques using modern AI frameworks.
Designed for graduate students, researchers, and healthcare technology professionals, this book bridges the gap between theoretical AI research and real clinical applications. It provides a comprehensive view of how intelligent systems can improve medical diagnosis, enhance healthcare collaboration, and support the next generation of data-driven medicine.
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