Generative AI for Healthcare
From Foundation Models to Clinical Integration
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Beschreibung
As generative artificial intelligence (AI) and foundation models rapidly transform healthcare, bridging the gap between algorithmic innovation and safe clinical integration has never been more critical. Generative AI for Healthcare provides a comprehensive, end-to-end guide to the next generation of AI in medicine and healthcare. Moving beyond traditional predictive analytics, this textbook explores how modern generative AI technology including large language models (LLMs), multimodality foundation models, and agentic frameworks are redefining clinical workflows, from automated medical documentation to personalized patient care.
Designed for data scientists, clinical informaticians, and medical researchers, the book systematically unpacks the transition from classical machine learning to state-of-the-art generative paradigms. Readers will explore the mechanics of deep learning architectures applied directly to complex healthcare data modalities like semi-structured Electronic Health Records (EHR), medical imaging, and continuous physiological signals. Crucially, the textbook emphasizes real-world application, offering actionable insights into clinical operations, workflow integration, regulatory frameworks, and the ethical deployment of AI to mitigate bias and data drift in live hospital environments and beyond.
By synthesizing technical rigor with clinical utility, this textbook equips readers with the practical skills to evaluate, deploy, and scale AI innovations that tangibly improve patient care. While a foundational understanding of basic statistics and healthcare data structures is recommended, the book’s intuitive framework, pairing technical concepts directly with concrete clinical input-to-output examples, makes advanced AI accessible. It is the essential blueprint for professionals preparing to lead the future of generative AI in healthcare.
As generative artificial intelligence (AI) and foundation models rapidly transform healthcare, bridging the gap between algorithmic innovation and safe clinical integration has never been more critical. Generative AI for Healthcare provides a comprehensive, end-to-end guide to the next generation of AI in medicine and healthcare. Moving beyond traditional predictive analytics, this textbook explores how modern generative AI technology including large language models (LLMs), multimodality foundation models, and agentic frameworks are redefining clinical workflows, from automated medical documentation to personalized patient care.
Designed for data scientists, clinical informaticians, and medical researchers, the book systematically unpacks the transition from classical machine learning to state-of-the-art generative paradigms. Readers will explore the mechanics of deep learning architectures applied directly to complex healthcare data modalities like semi-structured Electronic Health Records (EHR), medical imaging, and continuous physiological signals. Crucially, the textbook emphasizes real-world application, offering actionable insights into clinical operations, workflow integration, regulatory frameworks, and the ethical deployment of AI to mitigate bias and data drift in live hospital environments and beyond.
By synthesizing technical rigor with clinical utility, this textbook equips readers with the practical skills to evaluate, deploy, and scale AI innovations that tangibly improve patient care. While a foundational understanding of basic statistics and healthcare data structures is recommended, the book’s intuitive framework, pairing technical concepts directly with concrete clinical input-to-output examples, makes advanced AI accessible. It is the essential blueprint for professionals preparing to lead the future of generative AI in healthcare.
provides a complete educational arc around cutting edge generative AI ideal for university curricula features practical frameworks with clinical examples bridging complex AI technology with healthcare workflows goes beyond theory by detailing clinical MLOps EHR integration and safe deployment for real world healthcare use
Autor*in
Carl Yang
Themen in »Generative AI for Healthcare«
AI in Healthcare Biomedical Foundation Models Clinical Machine Learning Large Language Models (LLMs) Healthcare Machine Learning Operations (MLOps) Electronic Health Records (EHR) Integration Clinical NLP (Natural Language Processing) Medical Image Analysis Continuous Physiological Signal Processing Multimodality Representation Learning AI Ethics and Regulation in Healthcare