ISBN: 9798337378626 - medical llms for clinical safety assessment (7 resultados)

Condición: New.

Hardback. Condición: New.

Hardback. Condición: New.

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.

Hardcover. Condición: new. Hardcover. Large Language Models (LLMs) are increasingly being explored in clinical settings, where they promise to support tasks such as documentation, decision support, and patient communication. However, the integration of medical LLMs introduces significant clinical safety considerations, as errors, or inappropriate recommendations can have real-world consequences for patient care. Clinical safety assessment plays a critical role in evaluating whether these systems perform reliably and within acceptable risk boundaries across diverse clinical scenarios. This assessment requires different methods to test accuracy, bias, and failure modes, as well as alignment with clinical standards, regulatory expectations, and ethical principles, to ensure that medical LLMs augment. Medical LLMs for Clinical Safety Assessment provides a comprehensive overview of clinical safety evaluation frameworks for medical LLMs. It connects AI research with medical practice by providing evidence-based models. Covering topics such as LLMS, medical technologies, and clinical assessment, this book is an excellent resource for researchers, machine learning engineers, data scientists, health leaders, policy makers, academicians, and graduate students. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Large Language Models (LLMs) are increasingly being explored in clinical settings, where they promise to support tasks such as documentation, decision support, and patient communication. However, the integration of medical LLMs introduces significant clinical safety considerations, as errors, or inappropriate recommendations can have real-world consequences for patient care. Clinical safety assessment plays a critical role in evaluating whether these systems perform reliably and within acceptable risk boundaries across diverse clinical scenarios. This assessment requires different methods to test accuracy, bias, and failure modes, as well as alignment with clinical standards, regulatory expectations, and ethical principles, to ensure that medical LLMs augment. Medical LLMs for Clinical Safety Assessment provides a comprehensive overview of clinical safety evaluation frameworks for medical LLMs. It connects AI research with medical practice by providing evidence-based models. Covering topics such as LLMS, medical technologies, and clinical assessment, this book is an excellent resource for researchers, machine learning engineers, data scientists, health leaders, policy makers, academicians, and graduate students.…