Azadeh zamanifar (60 résultats)

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days.

Etat : New.

Etat : New.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days.

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : New. This book offers a comprehensive exploration into the role of Large Language Models (LLMs) in modern healthcare. It focuses specifically on the lifecycle of LLM deployment in healthcare settings, including transparency, accountability, data privacy, and regulatory compliance to ensure safe and effective use. By bridging the gap between technical artificial intelligence (AI) development and clinical application, this book highlights the critical collaboration between clinicians and data scientists to create representative datasets and fine-tune models for clinical accuracy and interpretability. Real-world challenges such as mitigating bias, managing AI hallucinations, and safeguarding patient confidentiality are explored, alongside strategies for continuous improvement and long-term impact assessment.Key features include:Case studies illustrating LLM applications in clinical decision support, medical imaging, patient communication, and administrative automationIn-depth discussion of data privacy, regulatory compliance, and ethical considerations in AI healthcare applicationsInsights into overcoming challenges like bias, hallucinations, and interoperability with existing health information systemsHow LLMs could revolutionize patient care in future, including operational efficiency and personalized medicineThis book is an essential resource for clinicians, healthcare executives, technologists, data scientists, and students seeking to harness the power of LLMs to improve patient outcomes and streamline healthcare delivery.…

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Application of Generative AI in Healthcare Systems | Azadeh Zamanifar (u. a.) | Taschenbuch | xiii | Englisch | 2026 | Springer | EAN 9783031829659 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Etat : New.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This is a comprehensive book that explores how explainable artificial intelligence (XAI), particularly large language models (LLMs), is transforming healthcare. The book covers foundational concepts of XAI, emphasizing the need for transparency, accountability, and interpretability in AI-driven medical systems, that are crucial for clinician and patient trust. It examines the principles and methodologies in explainable AI. It details how LLMs can make complex machine learning outputs understandable through explanations, model design, and human-centered description.Part of the book is dedicated to real-world applications, such as disease diagnosis, treatment planning, and patient management. It demonstrates how XAI improves clinical decision-making and patient outcomes. It discusses the integration of explainable LLMs into electronic health records (EHRs) and clinical workflows. It shows how these technologies facilitate data analysis, improve documentation, and support care. The book also addresses the challenges and limitations of deploying explainable LLMs in healthcare. It includes issues of privacy, data complexity, and adapting models to specific domains. Evaluation techniques for explainability are discussed, with attention to metrics, benchmarks, and human-centered assessment methods that ensure AI explanations are both accurate and clinically relevant. Ethical considerations, such as fairness, accountability, and privacy, are discussed. We highlight the importance of balancing transparency with patient confidentiality. The book provides case studies and empirical evidence illustrating the benefits and challenges of implementing XAI in real clinical settings.…

Hardcover. Etat : Brand New. 240 pages. 9.18x6.12x9.45 inches. In Stock.

Hardcover. Etat : Brand New. 312 pages. 9.18x6.12x9.45 inches. In Stock.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Generative AI has immensely influenced various fields, such as education, marketing, art and music, and especially healthcare. Generative AI can benefit the patient through various approaches. For instance, it can enhance the image qualities negatively affected by radiation reduction, preventing patients from needing to repeat the image-taking process. Also, the generation of one type of image from another more expensive one can help patients save funds. Generative AI facilitates the administrative process, letting the doctor focus more on the treatment process. It even goes further by helping medical professionals with diagnosis and decision- making, suggesting possible treatment plans according to the patient symptoms.This book introduces several practical GenAI healthcare applications, especially in medical imaging, pandemic prediction, synthetic data generation, clinical administration support, professional education, patient engagement, and clinical decision support, providing a review of efficient GenAI tools and frameworks in this area. GenAI empowers the treatment process through several methods; however, some ethical, privacy, and security challenges require attention. Despite the challenges presented, GenAI technological and inherited characteristics smooth the path of improvement for it in the future.…

Etat : New.

Etat : New.

Hardcover. Etat : Brand New. 224 pages. 9.25x6.10x9.21 inches. In Stock.

Hardback. Etat : New. This book offers a comprehensive exploration into the role of Large Language Models (LLMs) in modern healthcare. It focuses specifically on the lifecycle of LLM deployment in healthcare settings, including transparency, accountability, data privacy, and regulatory compliance to ensure safe and effective use. By bridging the gap between technical artificial intelligence (AI) development and clinical application, this book highlights the critical collaboration between clinicians and data scientists to create representative datasets and fine-tune models for clinical accuracy and interpretability. Real-world challenges such as mitigating bias, managing AI hallucinations, and safeguarding patient confidentiality are explored, alongside strategies for continuous improvement and long-term impact assessment.Key features include:Case studies illustrating LLM applications in clinical decision support, medical imaging, patient communication, and administrative automationIn-depth discussion of data privacy, regulatory compliance, and ethical considerations in AI healthcare applicationsInsights into overcoming challenges like bias, hallucinations, and interoperability with existing health information systemsHow LLMs could revolutionize patient care in future, including operational efficiency and personalized medicineThis book is an essential resource for clinicians, healthcare executives, technologists, data scientists, and students seeking to harness the power of LLMs to improve patient outcomes and streamline healthcare delivery.…

Etat : New.