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.
