From Foundation Models to Clinical Integration
Buch, Englisch, 315 Seiten, Format (B × H): 155 mm x 235 mm
ISBN: 978-981-9239-07-8
Verlag: Springer
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.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
- Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
.- Part I: Foundations of Generative AI in Healthcare
Generative AI has emerged as one of the most transformative developments in modern computing, enabling machines to synthesize text, images, clinical notes, predictions, and recommendations in previously unimaginable ways. In healthcare, these capabilities hold enormous potential for augmenting clinical decision-making, improving data accessibility, and supporting more equitable and personalized care.
Part I lays the technical and conceptual groundwork for the entire book. It introduces the fundamental principles behind generative modeling, covers the landscape of multimodal clinical data, and discusses the opportunities and limitations of using generative technologies in medicine. Readers will gain a shared vocabulary and conceptual framework that will prepare them for the more advanced material in later chapters.
.- Part II: Generative Models and Foundation Models
Generative models have evolved rapidly—from classical probabilistic approaches to deep generative frameworks such as variational autoencoders, generative adversarial networks, diffusion models, and large foundation models. In healthcare, these models serve a unique role: they enable clinicians and researchers to understand, synthesize, denoise, and augment complex data at scale.
Part II focuses on the design, training, and evaluation of generative models specifically in the clinical context. This includes modeling structured EHRs, medical imaging, physiological signals, clinical language, and multimodal data. The goal is to bridge theory and practice, showing how generative methods can meaningfully support diagnosis, forecasting, documentation, and research workflows, while acknowledging the constraints of clinical environments.
.- Part III: Agentic AI Systems for Healthcare
While generative models are powerful, they become truly impactful when embedded into agents—systems capable of reasoning, planning, interacting, and assisting clinicians across complex workflows. Agentic AI represents the next phase of deployment, enabling longitudinal, context-aware support that can coordinate tasks, query databases, retrieve evidence, manage clinical documentation, and interface with hospital systems.
Part III introduces the principles behind agent-centered design and illustrates how generative models can be orchestrated into robust, safety-aware AI agents for care delivery. Readers will learn about tool-using agents, retrieval-augmented agents, embodied agents, clinical decision agents, and safety guardrails. This part highlights not only the technical considerations but also the human–AI collaboration needed for successful clinical adoption.
.- Part IV: Clinical Applications and Use Cases
The value of generative and agentic AI ultimately lies in real-world clinical impact. Healthcare presents unique challenges—heterogeneous workflows, high-stakes decisions, limited time, privacy constraints, and a constant need for interpretability and trust.
Part IV examines how generative and agent-based AI systems can be applied across medical specialties, from radiology and ophthalmology to cardiology, oncology, intensive care, and population health. These chapters emphasize practical integration: what works, what doesn't, and what it takes to move from prototypes to reliable, regulated clinical tools. Readers will gain insight from real use cases, deployment examples, and emerging best practices across diverse domains.
.- Part V: Implementation, Policy, and the Future
As generative AI enters clinical practice, questions of safety, ethics, fairness, and governance become central. The healthcare sector operates under some of the strictest regulatory frameworks, and generative systems—particularly large, adaptive, and continuously learning models—challenge traditional approaches to validation and oversight. Models that perform well in retrospective studies may fail under distribution shifts, workflow constraints, or legal requirements. Moreover, healthcare institutions must balance innovation with trust, equity, and sustainability.
Part V addresses these issues head-on. It explores regulatory pathways, model auditing, monitoring, responsible AI principles, human-centered evaluation, and the importance of transparency across the model lifecycle. It also looks ahead to the future of generative and agentic AI in healthcare, outlining open challenges, research opportunities, and areas where technology, policy, and clinical practice must converge to ensure safe and equitable impact.
