Farhadi amirfarhad (39 resultados)

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

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

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Hardback. Condición: 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.…

Buch. Condición: 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. Condición: Brand New. 240 pages. 9.18x6.12x9.45 inches. In Stock.

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

Condición: New.

Hardback. Condición: 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.…

Hardcover. Condición: new. Hardcover. This book provides an in-depth exploration of AI safety in healthcare. It bridges the gap between technical innovation and patient protection. It examines emerging methodologies for risk management, transparency, and robust AI system design used specifically in medical applications.Readers will gain insights into the ethical considerations that surround AI deployment, techniques for improving the explainability and accountability of AI models, and the implementation of safety protocols to detect and prevent AI failures. The book also discusses the evolving regulatory view that shapes AI adoption and how privacy-preserving technologies can enable safer data sharing across institutions. In highlighting both current challenges and future directions, the book makes a meaningful contribution to the safe, responsible, and trustworthy integration of AI in healthcare systems. This is an illuminating read for researchers, practitioners, policymakers, and healthcare providers interested in acquiring the knowledge needed to harness AIs potential while safeguarding human health and well-being. The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condición: new. Hardcover. This book provides an in-depth exploration of AI safety in healthcare. It bridges the gap between technical innovation and patient protection. It examines emerging methodologies for risk management, transparency, and robust AI system design used specifically in medical applications.Readers will gain insights into the ethical considerations that surround AI deployment, techniques for improving the explainability and accountability of AI models, and the implementation of safety protocols to detect and prevent AI failures. The book also discusses the evolving regulatory view that shapes AI adoption and how privacy-preserving technologies can enable safer data sharing across institutions. In highlighting both current challenges and future directions, the book makes a meaningful contribution to the safe, responsible, and trustworthy integration of AI in healthcare systems. This is an illuminating read for researchers, practitioners, policymakers, and healthcare providers interested in acquiring the knowledge needed to harness AIs potential while safeguarding human health and well-being. The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condición: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 325 pp. Englisch. …

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Azadeh Zamanifar is currently head of computer engineering department at Islamic Azad university, science and research branch. She received her B.Sc. degree in Computer Engineering (Hardware) from Tehran University in 2002. She went on to complete.…