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Produktbild: Generative AI for Healthcare
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Generative AI for Healthcare From Foundation Models to Clinical Integration

Fr. 86.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

09.11.2026

Verlag

Springer Singapore

Seitenzahl

300

Maße (L/B)

23.5/15.5 cm

Sprache

Englisch

ISBN

978-981-9239-07-8

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

09.11.2026

Verlag

Springer Singapore

Seitenzahl

300

Maße (L/B)

23.5/15.5 cm

Sprache

Englisch

ISBN

978-981-9239-07-8

Herstelleradresse

Springer Singapore
No. 12-2F 101 Business Park
47100 Puchong, Selangor D.E.
MY
Email: sdc-bookservice@springer.com
Telephone: +49 6221 3454301
Fax: +49 6221 3454229

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  • Produktbild: Generative AI for Healthcare

  • .- 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.